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Job DescriptionGlobal Financial Firm located in Jersey City, NJ has an immediate contract opportunity for an experienced Java Developer. Pay Rate: $84.00 - $89.00 Negotiable based upon years of experienceSummary: • Work Mode: Hybrid, • Location: Jersey City, NJRequirements:, • Minimum 10 years of experience in the design and development of enterprise-grade Java-based systems., • Proficient in Java 17/21/25 LTS (Java 25 preferred), Core Java, Jakarta EE (J2EE), Spring Framework 7 / Spring Boot 4.x RESTful services., • Working knowledge of modern Java language features and JVM internals — GC tuning and performance profiling., • Expertise in databases such as Oracle or equivalent (PostgreSQL/MySQL) with strong SQL and PL/SQL programming skills; schema versioning via Flyway/Liquibase., • Proficient in using AI-assisted development tools such as Claude, GitHub Copilot, Devin, Cursor for code generation, review, and productivity., • Experience with multiple technologies and medium-to-complex distributed systems; well-versed with design standards, patterns, and enterprise frameworks., • Fast learner of new tools, frameworks, and technologies; comfortable working with distributed/cross-timezone teams., • Experience in event-driven architecture — Spring Boot components integrating with Apache Kafka and MQ (IBM MQ), including partitioning, consumer groups, idempotency, and transactional outbox patterns., • Excellent knowledge of Relational Databases, SQL, and ORM technologies (JPA 2/3, Hibernate 6) and connection pooling (HikariCP)., • Strong understanding of Spring Security 6 — OAuth2.0/OIDC, JWT-based stateless auth, RBAC/ABAC., • Familiarity with API design — REST with OpenAPI/Swagger, and exposure to high-performance service communication., • Resilience patterns using Resilience4j — circuit breaking, rate limiting, retries., • Caching and NoSQL exposure — Redis, MongoDB, Elasticsearch; awareness of vector databases (Pgvector) for AI/RAG-integrated services is a plus., • Exposure to Spring AI and LLM integration patterns (RAG workflows) is an added advantage., • Tools — Well versed with IntelliJ IDEA/Eclipse., • Test-Driven Development and automated testing: JUnit 5, Mockito, AssertJ, Cucumber/Gherkin, Testcontainers for integration testing, contract testing (WireMock/Spring Cloud Contract); JIRA, Maven/Gradle, SonarQube., • Experience with version control — GIT, Bitbucket, GitHub (including PR/branching workflows)., • Experience with Docker, multi-stage builds, Red Hat OpenShift, and Kubernetes (Deployments, ConfigMaps, Secrets, Ingress, HPA/KEDA autoscaling)., • Experience with CI/CD systems such as Jenkins, GitHub Actions, GitLab CI, or ArgoCD., • Observability exposure — Micrometer, Prometheus, Grafana, distributed tracing via OpenTelemetry/Zipkin/Jaeger., • Strong understanding of the full Software Development Life Cycle (SDLC)., • Experience in Agile methodologies — Scrum, sprint planning, and iterative delivery.

Job Description CoreWeave is The Essential Cloud for AI™. Built for pioneers by pioneers, CoreWeave delivers a platform of technology, tools, and teams that enables innovators to build and scale AI with confidence. Trusted by leading AI labs, startups, and global enterprises, CoreWeave combines superior infrastructure performance with deep technical expertise to accelerate breakthroughs and turn compute into capability. Founded in 2017, CoreWeave became a publicly traded company (Nasdaq: CRWV) in March 2025. Learn more at www.coreweave.com. What You'll Do: CoreWeave is constantly launching new services that change what customers can build on AI infrastructure, and the data layer is one of its most important new frontiers. As a Solution Specialist for Data Services, you are the person who opens that frontier. You identify new market opportunities where data architecture, mobility, and pipeline design unlock GPU adoption, and you drive the initial adoption of CoreWeave's newest data capabilities with the earliest customers across new industries. You turn what you learn in the field into direct input on the product roadmap, and you make the broader sales and solution architecture organization fluent in the value these new services create. About the role: As a Solution Specialist for Data Services, you operate at the leading edge of CoreWeave's go-to-market motion. Your job is not to run an established playbook; it is to create one. You take newly launched data services (high-throughput data movement, plus support for the databases, indexes, and data structures behind AI training, high-concurrency inference, and agentic workloads) into new accounts and new industries, prove their value with the first wave of customers, and establish the repeatable patterns that sales and solution architects use to scale adoption. Along the way you are the field's voice into engineering, translating what early customers need into the priorities that shape CoreWeave's data and storage roadmap. In this role, you will: • Own the commercial and technical strategy for net new customer wins in data infrastructure, where storage architecture, data mobility, and pipeline design are the primary buying triggers., • Drive new business opportunities where data migration complexity, egress costs, or multi-cloud sprawl are the barrier to GPU adoption at scale., • Translate customer requirements around data lakes, vector databases, and high-speed storage tiers into specific product feedback that shapes CoreWeave's storage roadmap., • Develop deal structures, technical playbooks, and benchmark narratives that help sales and SA teams accelerate storage-heavy opportunities., • Engage directly with enterprise buyers as the authoritative voice on data gravity, multi-cloud networking, and the total cost of moving workloads to CoreWeave., • Design the commercial framework for multi-petabyte data pipeline deals, including egress cost modeling and GPU utilization commitments, to support large enterprise closings., • Partner with capacity and infrastructure teams to maintain a competitive edge on storage efficiency, performance, and security across active and prospective customer deployments.Who You Are:, • 10+ years of experience in distributed systems, storage engineering, or data architecture, with a track record of applying that expertise to drive customer outcomes and revenue., • 5+ years working with multi-cloud networking and data transfer protocols (S3, rsync, WAN optimization) in a customer-facing or deal-shaping capacity., • Deep working knowledge of storage technologies including NVMe-over-Fabric, POSIX filesystems, and object storage, with the ability to translate that into commercial differentiation., • Experience deploying and tuning large-scale database clusters (SQL, NoSQL, or Vector) and positioning that capability against customer migration and modernization requirements., • Strong understanding of BGP, peering, and high-speed data interconnects as they relate to data gravity, egress cost modeling, and enterprise deal structure., • Familiarity with Kubernetes-native storage orchestration (CSI drivers, persistent volumes) and how it impacts workload portability and platform stickiness.Preferred:, • Experience driving new business or shaping product strategy in industries with high-density compute and data needs, such as Financial Services, Biotech and Life Sciences, or Media and Entertainment., • Prior background in technical sales, solution consulting, or product management supporting large-scale enterprise data infrastructure decisions., • Deep understanding of multi-cloud networking architecture and secure data egress and ingress strategies, particularly where compliance and data residency requirements are active deal variables., • Advanced degree in Computer Science or Engineering, or equivalent experience with a demonstrated ability to operate at the intersection of technical architecture and commercial strategy.Wondering if you're a good fit? Wondering if you're a good fit? We believe in investing in our people, and value candidates who can bring their own diversified experiences to our teams, even if you aren't a 100% skill or experience match. Here are a few qualities we've found compatible with our team. If some of this describes you, we'd love to talk. • You love translating boardroom goals into technical reality: you can explain high-level business strategy and low-level data architecture and storage tradeoffs in the same meeting., • You love acting as the bridge: between high-level sales strategy and deep storage and data-pipeline engineering., • You are an expert at navigating "The Room": you have a proven ability to manage complex, multi-stakeholder technical evaluations without losing momentum.Why CoreWeave? At CoreWeave, we work hard, have fun, and move fast! We're in an exciting stage of hyper-growth that you will not want to miss out on. We're not afraid of a little chaos, and we're constantly learning. Our team cares deeply about how we build our product and how we work together, which is represented through our core values: • Be Curious at Your Core, • Act Like an Owner, • Empower Employees, • Deliver Best-in-Class Client Experiences, • Achieve More Together We support and encourage an entrepreneurial outlook and independent thinking. We foster an environment that encourages collaboration and enables the development of innovative solutions to complex problems. As we get set for takeoff, the organization's growth opportunities are constantly expanding. You will be surrounded by some of the best talent in the industry, who will want to learn from you, too. Come join us! The base salary range for this role is $207,000 to $275,000. The starting salary will be determined based on job-related knowledge, skills, experience, and market location. We strive for both market alignment and internal equity when determining compensation. In addition to base salary, our total rewards package includes a discretionary bonus, equity awards, and a comprehensive benefits program (all based on eligibility). What We Offer The range we've posted represents the typical compensation range for this role. To determine actual compensation, we review the market rate for each candidate which can include a variety of factors. These include qualifications, experience, interview performance, and location. In addition to a competitive salary, we offer a variety of benefits to support your needs. The benefits below reflect our US-based offerings for full-time employees; for roles in other locations, benefits vary and are shared during the hiring process. These include: • Medical, dental, and vision insurance - 100% paid for by CoreWeave, • Company-paid Life Insurance, • Voluntary supplemental life insurance, • Short and long-term disability insurance, • Flexible Spending Account, • Health Savings Account, • Tuition Reimbursement, • Ability to Participate in Employee Stock Purchase Program (ESPP), • Mental Wellness Benefits through Spring Health, • Family-Forming support provided by Carrot, • Paid Parental Leave, • Flexible, full-service childcare support with Kinside, • 401(k) with a generous employer match, • Flexible PTO, • Catered lunch each day in our office and data center locations, • A casual work environment, • A work culture focused on innovative disruption California Applicants California Consumer Privacy Act Equal Opportunity & Accommodations CoreWeave is an equal opportunity employer, committed to fostering an inclusive and supportive workplace. All qualified applicants and candidates will receive consideration for employment without regard to race, color, religion, sex, disability, age, sexual orientation, gender identity, national origin, veteran status, or genetic information. As part of this commitment and consistent with the Americans with Disabilities Act (ADA), CoreWeave will ensure that qualified applicants and candidates with disabilities are provided reasonable accommodations for the hiring process, unless such accommodation would cause an undue hardship. If reasonable accommodation is needed, please contact: careers@coreweave.com. Export Control Compliance This position requires access to export controlled information. To conform to U.S. Government export regulations applicable to that information, applicant must either be (A) a U.S. person, defined as a (i) U.S. citizen or national, (ii) U.S. lawful permanent resident (green card holder), (iii) refugee under 8 U.S.C. § 1157, or (iv) asylee under 8 U.S.C. § 1158, (B) eligible to access the export controlled information without a required export authorization, or (C) eligible and reasonably likely to obtain the required export authorization from the applicable U.S. government agency. CoreWeave may, for legitimate business reasons, decline to pursue any export licensing process.

Job Description Job Title: Senior Network Automation Engineer - AI/ML, AIOps & Network Operations Location: Jersey City, NJ / Charlotte, NC / Chandler, AZ Duration: Contract - 12 months Pay Range: NJ2-101-08-01,Jersey City- $104.62 (Pay Rate: $73.67) -Preferred location NC1-025-05-03, Charlotte -$99.73 (Pay Rate: $70.23)-preferred location AZ1-805-03-80, Chandler- $99.73 (Pay Rate: $70.23) Job ID: 411704 About BCforward BCforward is a leading global IT consulting and workforce solutions firm providing services and support to Fortune 500 and government clients. Founded in 1998, BCforward has grown with our customers needs into a full-service business solutions provider. With delivery centers and offices across North America and India, we take pride in building long-term relationships and delivering excellence through innovation, collaboration, and integrity. Job Description We are seeking a Senior Network Automation Engineer - AI/ML, AIOps & Network Operations to join our dynamic team. The ideal candidate will have strong experience in Python-based backend development, network automation, telemetry integration, and applied AI/ML for operations and a proven ability to deliver safe, reliable automation and AI-assisted troubleshooting in production environments. Responsibilities: • Design and develop network automation services for event correlation, intelligent troubleshooting, and automated remediation., • Build automation that supports self-healing use cases with automated checks, safe remediation actions, and guided recovery steps., • Develop backend automation services using Python and Django with supporting data stores such as Redis for state and caching., • Integrate automation with monitoring, telemetry, and alerting systems to consume events and metrics in near real time., • Partner with Network Operations to identify repeatable operational patterns suitable for automation., • Collaborate with Monitoring and Telemetry engineers to ensure automation logic is driven by reliable data signals., • Implement guardrails, validation, and rollback logic to ensure safe production usage., • Contribute to testing, versioning, deployment, and operational support for automation services., • Provide production support and troubleshooting for automation services., • Document designs, runbooks, and usage guidelines for Operations teams., • Design and productionize ML solutions for anomaly detection, event correlation, incident classification, root-cause analysis, forecasting, and predictive maintenance., • Engineer and curate telemetry datasets, perform feature engineering, labeling, data quality validation, and model-ready transformations across metrics, logs, events, traces, and topology., • Develop AI-assisted troubleshooting using LLMs, RAG, embeddings, and enterprise knowledge sources with human oversight and deterministic controls., • Build and maintain MLOps pipelines for experiment tracking, validation, versioning, deployment, monitoring, retraining, and rollback., • Evaluate models using precision, recall, false-positive reduction, resolution accuracy, latency, reliability, and operational adoption., • Implement responsible AI controls including explainability, privacy, security, bias assessment, auditability, access control, drift monitoring, and safe fallback behavior., • Create APIs and reusable services that integrate AI/ML inference with event management, observability, orchestration, and ITSM platforms. Required Skills & Qualifications: • Strong software development experience in Python, including REST APIs, message queues, or workflow engines., • Experience building backend services using Django or similar frameworks., • Hands-on experience with Redis or equivalent in-memory data stores., • Solid understanding of enterprise networking including routing, switching, wireless, and SD-WAN concepts., • Experience integrating automation with monitoring, alerting, or telemetry systems., • Ability to design automation for reliability and safety in production environments., • Experience operating in high availability, failure-sensitive environments., • Strong communication and collaboration with engineering and operations teams., • Education: Bachelor's or Master's in Computer Science, Engineering, or related field., • Experience: 8-12 years in network engineering, operations, automation, telemetry, monitoring, data engineering, or applied analytics with production AI/ML delivery. Preferred Skills: • Experience with event correlation, rule-based automation, or decision workflows., • Familiarity with observability data, including metrics, logs, and events, as automation inputs., • Exposure to Infrastructure as Code and CI/CD practices., • Understanding of IT service management and incident workflows., • AIOps experience in network assurance, observability, incident reduction, or autonomous operations., • Experience with cloud AI/ML services, GPU workloads, or enterprise model platforms., • Familiarity with graph analytics or knowledge graphs for topology-aware correlation and root-cause analysis., • Experience designing evaluation datasets, offline and online tests, and operational acceptance criteria for AI-assisted automation., • Hands-on ML stack: pandas, NumPy, scikit-learn, and PyTorch or TensorFlow. Familiarity with Jupyter, MLflow or similar, containers, Kubernetes, and CI/CD. Experience with vector databases or enterprise search, and frameworks such as LangChain or Semantic Kernel. Role Logistics & Notes: • Approved sites and W2 rates: NJ2-101-08-01, Jersey City, NJ ($104.63/hr, preferred); NC1-025-05-03, Charlotte, NC ($99.74/hr, preferred); AZ1-805-03-80, Chandler, AZ ($99.74/hr)., • Hybrid schedule with a minimum of 3 days onsite per week., • Start month: As soon as a candidate is identified., • Interview process: 1-2 rounds. Video required. In-person required. Glider ID verification., • Contract-to-hire potential. Candidates must not require sponsorship now or in the future., • On resume: include current location, relocation plan, availability/start date, and cooling-off requirement if applicable. Why BCforward? At BCforward, we believe in advancing lives and careers. When you join our team, you gain access to: • Competitive compensation and benefits., • Opportunities for growth with global clients., • A supportive, inclusive culture that values innovation and people., • Exposure to cutting-edge technologies and projects. About Our Commitment BCforward is an equal opportunity employer. We value diversity and are committed to creating an inclusive environment for all employees. All qualified applicants will receive consideration for employment without regard to race, color, religion, gender, sexual orientation, gender identity, national origin, age, disability, or veteran status. Interested? Apply Now! If this sounds like the right opportunity for you, please apply with your most recent resume.

Job DescriptionJob Title: AI Engineer Location: Newark, NJ (Hybrid – 3 days onsite) Duration: 6 months Salary Range: $56.00 - $58.00/Hour on W2 (Without Benefits). Applicants must be willing to work on W2 Job Description: Candidate Requirements: • Microsoft Certified: Azure AI Engineer Associate or Azure AI Apps and Agents Developer Associate will be given priority compared to other candidates., • If the candidate is certified, include the respective Microsoft Certification link in the resume. Without that certificate credential, the certification is not accepted.Role Overview:, • An Azure AI Foundry AI Engineer designs, builds, and deploys intelligent generative AI, agentic workflows, and RAG (Retrieval-Augmented Generation) applications., • This role requires coding with Python, orchestrating AI models, and adhering to responsible AI standards for enterprise scalability.Key Responsibilities: AI & Agent Development: • Design autonomous or semi-autonomous AI agents and RAG pipelines using Azure AI Foundry (formerly Azure AI Studio).Model Orchestration & Integration:, • Build and fine-tune large language models (LLMs) and integrate them with business applications, Copilot Studio, and data pipelines.Testing & Evaluation:, • Implement performance and evaluation tooling (such as RAGAS or TruLens) to assess grounding accuracy, reduce hallucinations, and ensure model explainability.Infrastructure Management:, • Develop scalable AI infrastructure and maintain reusable AI components in accordance with engineering best practices (version control, observability, CI/CD).Core Requirements & Skills: Technical Skills: • Proficiency in Python, prompt engineering, and utilizing frameworks like LangChain, Semantic Kernel, or crewAI.Cloud Experience:, • Deep understanding of the Microsoft Azure ecosystem, including Azure OpenAI Service, Azure Machine Learning, and Microsoft Fabric.AI Governance:, • Strong focus on Responsible AI and Model Context Protocol (MCP) to ensure security, privacy, and fairness in model outputs.Experience:, • Typically requires a bachelor’s degree in computer science or a related field, alongside 2–5+ years of software or AI/ML engineering experience., • 7+ years in Microsoft Azure.Programming & Machine Learning:, • Python (primary language)., • ML frameworks: TensorFlow, PyTorch, Scikit-learn., • Data pipelines and preprocessing., • Machine learning, deep learning, and statistics., • Data modeling and feature engineering.Model Deployment & MLOps:, • Model deployment and MLOps (e.g., MLflow, Docker, CI/CD).Development & Integration:, • APIs and microservices.Cloud Platforms:, • Azure ML, SageMaker, Vertex AI.Generative AI:, • LLMs (GPT, Llama, etc.)., • RAG (Retrieval-Augmented Generation)., • Prompt engineering and vector databases.

Job Description Tiger Analytics is a fast-growing advanced analytics consulting firm. Our consultants bring deep expertise in Data Science, Machine Learning and AI. We are the trusted analytics partner for several Fortune 100 companies, enabling them to generate business value from data. Our business value and leadership has been recognized by various market research firms, including Forrester and Gartner. We are looking for top-notch talent as we continue to build the best analytics global consulting team in the world. We are seeking an experienced Data Engineer to join our data team. In this role, you will be responsible for designing, building, and maintaining scalable data pipelines, data integration processes, and data infrastructure on AWS cloud. You will collaborate closely with data scientists, analysts, and AI teams to support analytics, machine learning, and Generative AI initiatives across the organization. Requirements Key Responsibilities • Design, develop, and deploy end-to-end data pipelines on AWS cloud infrastructure using services such as Amazon S3, AWS Glue, AWS Lambda, Amazon Redshift, etc., • Implement data processing and transformation workflows using Databricks, Apache Spark, and SQL to support analytics, reporting, and AI-driven use cases., • Build and maintain orchestration workflows using Apache Airflow to automate data pipeline execution, scheduling, and monitoring., • Support data preparation and ingestion for AI/ML and Generative AI workloads, including handling structured and unstructured datasets., • Enable data pipelines that support LLM-based applications, vector embeddings, and knowledge retrieval systems., • Lead the migration of legacy data systems to modern cloud-based data architectures., • Develop and maintain CI/CD pipelines for data workflows and platform automation., • Collaborate with data scientists, ML engineers, and AI teams to ensure data availability for model training, inference, and GenAI applications., • Optimize data pipelines for performance, reliability, scalability, and cost-effectiveness using AWS best practices. Required Skills • 8+ years experience as a Data Engineer working with AWS cloud services., • Hands-on experience with AWS services such as S3, Glue, Lambda, Redshift, and related data platform tools., • Experience building data pipelines using Databricks, Apache Spark, and SQL., • Experience with Apache Airflow for workflow orchestration., • Strong understanding of data modeling, data lake/lakehouse architectures, and ETL/ELT frameworks., • Experience with CI/CD pipelines and version control systems (Git)., • Exposure to Generative AI or LLM-based applications., • Experience supporting data pipelines for AI/ML workloads., • Familiarity with vector databases, embeddings, and Retrieval-Augmented Generation (RAG) architectures., • Experience working with LLM APIs or AI frameworks such as LangChain., • Understanding of MLOps workflows and model deployment pipelines. Benefits Significant career development opportunities exist as the company grows. The position offers a unique opportunity to be part of a small, challenging, and entrepreneurial environment, with a high degree of individual responsibility. Tiger Analytics provides equal employment opportunities to applicants and employees without regard to race, color, religion, age, sex, sexual orientation, gender identity/expression, pregnancy, national origin, ancestry, marital status, protected veteran status, disability status, or any other basis as protected by federal, state, or local law.

Job Description Description: Job Description Data / AI Native Engineer Location: St. louis, MO/ Arlington, VA/ NYC, NY & NJ Remote is OK | Arlington & NY/NJ is hybrid Day-to-Day Job Duties: Design, build, optimize, and support enterprise-scale Databricks and AWS Lakehouse data pipelines using Spark and PySpark. Develop Databricks solutions leveraging Delta Lake, Medallion Architecture (Bronze/Silver/Gold), and Unity Catalog. Develop high-volume batch and streaming pipelines using PySpark, ensuring scalability, performance, reliability, data quality, and maintainability. Design and implement modern lakehouse engineering patterns including SCD Type 2, Change Data Capture (CDC), idempotent writes, schema evolution, and data contracts. Work across the AWS data ecosystem, including technologies such as S3, Glue, Lambda, EMR, Redshift, Kinesis, and related AWS services. Use GitHub Copilot and/or Claude Code CLI as part of the daily engineering workflow to accelerate application code, data-pipeline development, testing, debugging, optimization, and documentation. Rapidly develop proofs of concept and MVPs using AI-assisted development, while establishing clear paths to scalable production implementation. Apply AI capabilities directly within the data-engineering lifecycle, including use cases such as: AI-assisted schema inference Automated data-quality rule generation LLM-based anomaly detection AI-generated PySpark transformations Intelligent data validation and enrichment Build data foundations for AI-enabled products, including feature stores, vector stores, RAG data layers, embedding pipelines, and other AI-ready data architectures. Design and develop high-volume streaming pipelines using technologies such as Spark Structured Streaming, Kafka, Kinesis, and Databricks Auto Loader. Develop and orchestrate enterprise data pipelines using technologies such as Databricks Workflows, Delta Live Tables, Airflow, and dbt. Implement secure and governed data-engineering practices using Unity Catalog, including catalog/schema organization, access control, lineage, governance, and data discovery. Lead technically complex data-engineering initiatives, drive solution design, coordinate engineering activities, and help resolve complex platform and pipeline issues. Partner with architects, software engineers, product owners, source-system teams, governance teams, analytics teams, AI teams, and business stakeholders. Remain actively hands-on with PySpark, Databricks, AWS, and software-engineering activities, rather than operating solely as a people manager or high-level data architect. Basic Qualifications: Minimum 8 years of overall Data Engineering / Software Engineering experience delivering enterprise-scale data solutions. Minimum 5 years of strong hands-on Data Engineering experience, including design, development, optimization, and support of production data pipelines. Minimum 3 years of hands-on Databricks experience, including production experience with: Delta Lake Medallion Architecture Bronze / Silver / Gold Unity Catalog Minimum 5 years of hands-on Spark experience, with strong expertise in PySpark specifically, rather than Scala-only Spark development. Minimum 3 years of AWS data-platform experience, using services such as S3, Glue, Lambda, EMR, Redshift, Kinesis, or related AWS technologies. Minimum 2 years of experience leading complex data-engineering initiatives, technical workstreams, or engineering teams while remaining hands-on technically. Proven experience combining data engineering and software-engineering practices, including modular development, testing, source control, CI/CD, observability, and production support. Demonstrated daily hands-on use of GitHub Copilot and/or Claude Code CLI, with the ability to describe concrete examples of how these tools are used to accelerate engineering work. Demonstrated ability to build POCs and MVPs rapidly using AI-assisted development. Strong understanding of distributed data-processing principles, large-scale pipeline design, performance optimization, data quality, and production operations. Strong SQL skills and experience working with large enterprise datasets. Critical Screening Requirements: Candidates should be screened out if they do not demonstrate: Proven experience spanning Data Engineering and Software Engineering. Proven experience leading complex data-engineering initiatives or teams. Hands-on Databricks experience including Delta Lake, Bronze/Silver/Gold Medallion Architecture, and Unity Catalog. High-volume Spark and PySpark experience; Scala-only Spark experience is not sufficient. Strong experience with the AWS data ecosystem. Daily hands-on use of GitHub Copilot and/or Claude Code CLI with specific examples. Ability to use AI-assisted development to rapidly create POCs and MVPs. Travel Travel as required based on project and client needs. Degree Bachelor's degree in Computer Science, Data Engineering, Information Systems, Engineering, or a related technical discipline, or equivalent professional work experience. Nice to Have (But Not a Must) 2 years of experience applying AI/Generative AI within data-engineering solutions, particularly: AI-assisted schema inference Automated data-quality rules LLM-based anomaly detection AI-generated PySpark transformations Experience building AI-enabled data products, including feature stores, vector stores, RAG data layers, and embedding pipelines. 2 years of high-volume streaming experience using Spark Structured Streaming, Kafka, Kinesis, Auto Loader, or equivalent technologies. Strong implementation experience with modern lakehouse patterns including: SCD Type 2 CDC Idempotent writes Schema evolution Data contracts Hands-on experience with dbt, Airflow, Databricks Workflows, and/or Delta Live Tables. Experience designing data platforms and pipelines supporting enterprise-scale GenAI, RAG, machine-learning, or agentic-AI solutions. Profiles That Typically Align Well Senior Data Engineer Lead Data Engineer Principal Data Engineer Data Platform Engineer Senior Analytics Engineer Data Engineering Manager with active individual-contributor responsibilities Profiles That Typically Do Not Align Traditional ETL Developer focused primarily on Informatica / Ab Initio / DataStage without cloud-native engineering BI / Report Developer Diagram-only Data Architect DBA Hadoop Administrator

Job DescriptionApplied AI Researcher New Jersey, Jersey city GenAI / NLP / Agentic AI / Applied Machine Learning LevelResearch-focused Individual ContributorTarget / alternate titlesApplied Scientist; Research Scientist - NLP; AI Research Scientist; ML Researcher; NLP Scientist; GenAI Researcher; Applied ML ScientistCore keywordsapplied research, LLM, NLP, transformers, RAG, retrieval, model evaluation, experiments, robustness, embeddings, synthetic data, multimodal, PyTorch, Hugging Face, banking AI, AWS AI, AIRPRecruiter red flagsAcademic-only profile with no applied delivery; weak experimental design; cannot translate research into AIRP-ready business or engineering requirements; no awareness of regulated data constraints. Role purpose Bridge advanced AI research and practical enterprise use cases by validating models, methods, and prototypes that can become production-grade AIRP solutions. The role focuses on measurable business value, rigorous experimentation, model behavior, and safe translation of research into banking-relevant applications. Client-specific emphasis • Research must be grounded in enterprise business use cases, not generic AI experimentation., • Candidates should understand how model, retrieval, data, evaluation, latency, cost, and safety decisions affect production delivery on AIRP., • Cloud/AWS awareness is valuable because successful research outputs must be handed off to engineering teams building on AWS-hosted AIRP.Primary ownership, • Applied research agenda for LLMs, NLP, RAG, evaluation, multimodal AI, and agentic workflows relevant to enterprise use cases., • Prototypes, experiments, benchmark design, model-selection recommendations, and production-readiness evidence., • Research-to-production handoff with AI engineering, AIRP platform, product, risk, and governance teams.Key responsibilities, • Conduct applied research in LLMs, GenAI, NLP, information retrieval, multimodal AI, synthetic data, and agentic AI., • Design experiments to evaluate model performance, robustness, safety, scalability, interpretability, enterprise usefulness, and production feasibility., • Prototype AI solutions for KYC, credit underwriting, governance tracking, pitch book generation, Banker 360, Customer 360, deal library intelligence, financial crime quality, and sanctions screening., • Develop evaluation methodologies using golden datasets, adversarial testing, offline benchmarks, human review, business outcome metrics, and risk-specific acceptance criteria., • Assess prompt optimization, RAG, fine-tuning, instruction tuning, synthetic data generation, distillation, and model adaptation techniques., • Document model limitations, data assumptions, hallucination patterns, bias risks, performance boundaries, and control recommendations for regulated deployment., • Collaborate with engineers to convert prototypes into production-ready AIRP requirements, including latency, cost, observability, security, and AWS/cloud deployment considerations., • Track emerging AI research and translate relevant advances into practical recommendations for the enterprise.Must-have candidate profile, • Advanced degree preferred, usually MS or PhD in AI, ML, computer science, statistics, computational linguistics, mathematics, or related field., • Strong foundation in machine learning, deep learning, NLP, transformers, information retrieval, and generative AI., • Hands-on experience with LLMs, embeddings, RAG, model evaluation, and applied GenAI experimentation., • Python skills with PyTorch, TensorFlow, Hugging Face, scikit-learn, or equivalent research frameworks., • Ability to design rigorous experiments and communicate findings to technical, product, business, risk, and governance stakeholders., • Ability to translate research results into production requirements suitable for an AWS-hosted enterprise platform.Preferred experience, • Research or applied science experience in banking, finance, compliance, risk, legal, operations, financial crime, sanctions, or enterprise knowledge systems., • Experience with AWS Bedrock, SageMaker, vector search, MLflow, Databricks, model evaluation tooling, or cloud-based experimentation environments., • Publications, patents, internal research contributions, open-source AI contributions, or prior research-to-production handoffs., • Familiarity with Responsible AI, model validation, privacy constraints, audit documentation, and regulated deployment environments. Regards, Sr. Technical Recruiter Shubham Chandra Shubham.c@echoitsol.com

Job Description Ampcus Inc. is a certified global provider of a broad range of Technology and Business consulting services. We are in search of a highly motivated candidate to join our talented Team. Job Title: AI Senior Security Engineer Location(s): Jersey City, NJ, Tampa, FL or Phoenix AZ Job Summary Seeking a highly motivated Security Engineer to design, develop, and deploy autonomous AI agents that reduce manual overhead and drive intelligent decision-making across cybersecurity operations while ensuring appropriate guardrails, security controls, data privacy, and human oversight. Key Responsibilities • Design, build, and maintain AI agents capable of multi-step reasoning and tool use to automate cybersecurity use cases, including autonomous incident investigation and vulnerability prioritization., • Develop advanced prompt engineering solutions using high-precision, version-controlled prompts and programmatic techniques to optimize LLM outputs for reliable security logic and automated remediation., • Use and fine-tune machine learning models, including deep learning, K-Means, SVM, and related techniques, to identify sophisticated attack patterns that bypass traditional signature-based defenses., • Integrate large language models from providers such as OpenAI, Anthropic, and open-source platforms into production environments through APIs or local deployments., • Develop and optimize data ingestion pipelines, with a particular focus on vector databases and AI-ready data processing., • Audit existing manual workflows and re-engineer them using a machine-first approach, leveraging LLMs to process and analyze unstructured data., • Deploy, monitor, and optimize AI solutions using cloud-native technologies, ensuring high availability, low latency, scalability, and cost efficiency., • Implement AI safety and security guardrails to ensure appropriate outputs, ethical standards, data privacy, and human-in-the-loop checkpoints where required. Required Qualifications • Hands-on experience with Python, including data ingestion, data cleaning, machine learning libraries, and system and web-based integrations., • Proficiency with machine learning frameworks and libraries, preferably Scikit-Learn and PyTorch., • Experience with AI agent orchestration frameworks such as LangChain, CrewAI, or AutoGPT., • Hands-on experience developing and tuning machine learning models for a range of cybersecurity use cases., • 3–4 years of hands-on experience with at least one cloud-based AI platform, such as AWS Bedrock, AWS SageMaker, or Azure AI., • Experience with Docker, Kubernetes, and Git for deployment and version control., • Strong experience with RESTful and GraphQL APIs for integrating disparate systems., • Experience designing and implementing AI/ML solutions in cybersecurity environments. Education • Bachelor's or Master's degree in Cybersecurity, Computer Science, Artificial Intelligence, or a related field., • Relevant industry certifications and equivalent practical work experience may be considered. Preferred Certifications • GIAC Machine Learning Engineer, • AWS Certified Machine Learning Engineer, • Microsoft Certified: Azure AI Engineer Associate, • Certified Information Systems Security Professional (CISSP) Ampcus is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, protected veterans or individuals with disabilities.

Job DescriptionDescription: AI Cloud Engineer - RTH Location: Hybrid | Experience: 3-5 years | Function: AI Engineering & Cloud Platforms ABOUT THE ROLE We are seeking a GenAI Cloud Engineer to build and deploy enterprise-scale agentic AI solutions that support complex business workflows, decision support, operational intelligence, customer engagement, and other signal-to-action use cases across the enterprise. This is a hands-on engineering role responsible for designing, developing, and delivering production-grade AI agents using AWS Bedrock, Amazon Bedrock AgentCore, enterprise data products, semantic layers, and governed cloud-native architectures. You will implement agent runtimes, orchestration patterns, memory, tool integration, observability, evaluation, and guardrail capabilities while partnering with architects, data teams, product owners, and Responsible AI stakeholders. The ideal candidate has recent experience building enterprise agents, agentic RAG, multi-agent workflows, and secure integrations with business systems in regulated environments. KEY RESPONSIBILITIES Develop cloud-native microservices, APIs, and event-driven components using Python, JavaScript/TypeScript, FastAPI, AWS Lambda, EventBridge, and related AWS services. Build and deploy enterprise AI agents using Amazon Bedrock AgentCore, Bedrock models, AgentCore runtime services, AgentCore Gateway, agent registry patterns, and agent-to-agent communication where appropriate. Implement data ingestion, semantic retrieval, embeddings, vector search, knowledge graph, and memory pipelines that allow agents to consume certified data products, Denodo/USBIE sources, Bedrock Knowledge Bases, and governed enterprise content. Develop intent classification, tool routing, workflow orchestration, guardrails, policy checks, human-in-the-loop controls, and fail-safe escalation patterns for agentic applications. Integrate agents with enterprise systems and workflows such as Salesforce/Elevate, Service Platform, CaseView, Genesys, MAC/MPI, USBIE, Denodo, APIs, MCP tools, and event streams. Build evaluation, monitoring, tracing, and operational controls for agents, including quality metrics, hallucination and safety checks, latency, cost, feedback loops, and incident response readiness. Uphold enterprise standards for application security, IAM/agent identity, data protection, auditability, Responsible AI, ADS/data-product governance, observability, and production reliability. Stay current with AWS AgentCore, Bedrock, agent frameworks, model evaluation approaches, memory architectures, semantic data products, and enterprise agentic AI patterns. LEADERSHIP, MENTORING & INFLUENCE (ONLY FOR SENIOR 5-8 YEARS OF EXPERIENCE) Lead a workstream: Take technical ownership of a defined feature area or service - from design through production support - coordinating a small group of engineers where needed. Mentor engineers: Guide associate and mid-level engineers through code review, pairing, and design feedback; help them build GenAI and cloud skills. Contribute to architecture: Participate actively in design reviews, propose solution options with clear trade-offs, and help shape the patterns and standards the team adopts. Influence through delivery: Partner directly with product owners, architects, and business stakeholders to refine requirements and build credibility through working software. Improve the team: Identify and drive improvements in engineering practices, tooling, reusable components, and documentation; share knowledge through demos and internal write-ups. REQUIRED QUALIFICATIONS 3-5 years of software engineering experience, with hands-on delivery of cloud-native applications or AI/ML-enabled platforms. Strong proficiency in Python and JavaScript/TypeScript, including API development, asynchronous processing, test automation, and production debugging. Experience developing microservices, APIs, event-driven workflows, distributed applications, and secure integrations with enterprise systems. Hands-on experience with AWS cloud services, including serverless, container, messaging/eventing, IAM, observability, and cloud-native deployment patterns. Recent experience building enterprise GenAI or agentic AI solutions, including agents, agentic RAG, multi-step orchestration, chat experiences, or signal-to-action workflows. Experience with Amazon Bedrock, Amazon Bedrock AgentCore, or comparable enterprise LLM and agent runtime platforms. Experience with LangGraph, LangChain, Strands, MCP, A2A protocols, tool-use frameworks, or comparable multi-agent orchestration patterns. Experience with model evaluation, prompt engineering, agent quality testing, feedback loops, LLM-as-judge patterns, and reliability measurement. Experience with data ingestion, vector databases, embeddings, semantic search, knowledge bases, memory stores, and retrieval patterns for structured and unstructured data. Experience implementing AI guardrails, safety controls, intent classification, routing logic, access controls, audit logging, evaluation, and human escalation paths. Strong understanding of software engineering best practices, application security, Responsible AI expectations, data governance, scalability, resilience, and production operations. Experience mentoring engineers, leading technical delivery of a defined workstream, and collaborating with architects, product owners, data teams, and platform teams. PREFERRED QUALIFICATIONS Experience with event-driven and serverless architectures using AWS EventBridge, MSK, SNS/SQS, Step Functions, Lambda, API Gateway, and container services. Experience delivering AI or data products in financial services, insurance, or other regulated environments with privacy, compliance, audit, and control requirements. Familiarity with enterprise data products, semantic layers, Denodo, USBIE, ADS certification, knowledge graphs, Neptune/OpenSearch, and governed data consumption patterns. Experience integrating agents with CRM, service, telephony, document, workflow, or engagement platforms such as Salesforce, Genesys, Service Platform, MAC/MPI, or similar systems. IDEAL CANDIDATE You are a strong software engineer with recent enterprise GenAI and agentic AI experience who enjoys turning ambiguous business use cases into reliable, governed, production-ready agents. You understand that CNS agents must combine data products, signals, deterministic rules, LLM reasoning, memory, tool use, observability, and human controls. You can work across cloud architecture, data integration, AI orchestration, and production operations while raising the technical bar for the team. LEVELING: SENIOR VS. DEV Senior GenAI Cloud Engineer - 5–8 years. Owns architecture and technical direction for enterprise agentic AI capabilities, sets standards for AgentCore, data, governance, observability, and multi-agent patterns, leads complex multi-team initiatives, mentors senior engineers, and influences cross-team design decisions. GenAI Cloud Engineer - 3+ years. Hands-on technical contributor. Leads implementation of specific agent workstreams, contributes to AgentCore architecture and delivery patterns, mentors associate and mid-level engineers, and influences within the team through working software. Rate: $60-65/hr #LI-PP1

Through our Residential and Commercial business units, Vector Security delivers smart security, life safety, video, access control, and monitoring solutions. Vector Security Networks (VSN). Vector Security is a Drug-Free Workplace.

About the Role The Applied AI Engineer turns research and off-the-shelf models into systems the business actually runs on, pricing, risk, research workflows, or internal tooling. If a model ships but never gets integrated into a real workflow, it doesn't matter how good it is; this role is the difference between a promising prototype and something a portfolio manager touches every day. What You'll Do • Design, build, and ship production AI features end to end, from model selection through integration and monitoring, • Fine-tune and evaluate LLMs and other models against real business tasks, not generic benchmarks, • Build the evaluation harnesses and guardrails needed to trust a model's output in a production setting, • Work directly with researchers, PMs, and engineers to translate a vague idea into a shipped tool, • Own the full lifecycle of what you build: performance, cost, reliability, and iteration Must-haves • 3+ years shipping production software, with hands-on experience building applied AI or ML-backed features, not just research notebooks. This is non-negotiable for the role, • Strong Python and solid engineering fundamentals: testing, version control, code review, • Practical experience with LLM APIs, fine-tuning, or retrieval-augmented systems, • Comfortable owning a project from ambiguous spec to production Nice-to-haves • Experience in a regulated or high-stakes environment (finance, healthcare) where correctness and auditability matter, • Familiarity with vector databases, embeddings, or agentic workflows, • Prior exposure to a quant or trading environment, • Open-source contributions or public writing on applied AI work Why This Role This is a seat where the work ships fast and gets used immediately, not a research role that ends at a slide deck. The team is small enough that one engineer's judgment on what to build, and what not to, actually shapes the roadmap.

Founding AI Lead, Forward Deployed Engineering About the Opportunity A leading global alternative asset management firm, with more than $300 billion in assets under management and teams across private equity, impact, credit, real estate, and market solutions, is hiring a founding AI lead to help build a new Forward Deployed Engineering (FDE) team. This is a builder-leader role. You'll set up the function from scratch: hiring the team, defining how it operates, and setting the technical bar. You'll also stay hands-on, writing code and shipping production-ready AI products inside the firm's highest-value workflows. You'll start with a small team of VP-level engineers and scale the capability as demand and impact grow. You'll work directly with Partners, Managing Directors, and department heads to find the problems worth solving, shape ambiguous opportunities into real plans, and decide when a prototype is ready to become a product. What You'll Do • Build and lead the FDE function, starting with VP-level engineers. Hire, mentor, and retain strong engineers, and set the culture, standards, and talent model as the team grows., • Manage team capacity, prioritization, and resource allocation to maximize business impact across the FDE portfolio., • Define the operating model: how opportunities are identified, prioritized, scoped, evaluated, shipped, adopted, handed off, scaled, or retired., • Build AI products, digital coworkers, and workflow solutions that solve meaningful business problems across the firm., • Guide the team on when to use agentic workflows, multi-agent architectures, retrieval systems, automation, traditional software, process redesign, or simply recommend not building at all., • Set technical standards for AI products, including architecture patterns, security, access controls, observability, documentation, support expectations, and production readiness., • Establish firm-wide standards for AI evaluation, reliability, and release readiness, covering quality, safety, business effectiveness, regression testing, and model or workflow drift., • Partner with Security, Compliance, Legal, Data, Infrastructure, and application teams so AI solutions respect information barriers, entitlement models, audit requirements, and secure development practices., • Partner with senior stakeholders to set clear expectations on what AI systems can and cannot do. What We're Looking For • 10+ years of experience in software engineering or AI engineering and experience leading teams and delivering complex enterprise solutions., • Bachelor's degree in Computer Science, Engineering, Information Systems, or a related technical field (equivalent practical experience considered)., • A proven record of building teams and setting technical standards, raising the bar without creating unnecessary bureaucracy., • Experience building, scaling, or transforming an engineering function, platform, or capability, including the standards, talent model, and operating cadence, • Hands-on AI application development, including model integration, context and retrieval design, evaluation frameworks, agentic workflows, workflow orchestration, human-in-the-loop processes, and production deployment., • Experience building AI solutions on unstructured data such as investment memos, CIMs, credit agreements, LP communications, contracts, financial statements, and presentations., • Strong production engineering discipline: code review, testing, version control, CI/CD, secure development, documentation, and operational ownership., • A product mindset: you identify the underlying problem, weigh alternative approaches, and deliver solutions people actually adopt., • The ability to earn trust quickly with senior stakeholders, communicate trade-offs clearly, and turn ambiguous business needs into an executable technical plan., • Financial services fluency, with interest in or experience with investment, fund operations, or financial services workflows. Technology The team is technology agnostic and picks the right tool for each problem. You'll need credibility in modern AI application development and enterprise-grade software delivery. The team works with a range of technologies, including: • Python, TypeScript, JavaScript, Java, .NET, or similar languages, • OpenAI, Anthropic, Gemini, Azure AI, Microsoft Copilot, and Copilot Studio, • Agentic and multi-agent architectures, tool calling, orchestration frameworks, and RAG, • Evaluation, observability, and model monitoring frameworks, • APIs, integrations, workflow automation, and distributed systems, • Azure, AWS, Google Cloud, and cloud-native architectures, • Databricks, SQL Server, vector databases, and modern data platforms, • CI/CD, test automation, and secure development practices Why Join? • Founding role: You'll build the function, its standards, and its culture from the ground up, with real input on how AI gets built and deployed across the firm., • High-value problems: Your work will sit inside core investment and fund workflows, with direct exposure to senior investment professionals and operating leaders., • Ownership: You decide what gets built, what gets bought, and what gets left alone, and you own the path from prototype to production., • Real constraints, real impact: Information barriers, entitlements, and audit requirements are part of the job, so what you ship has to work in a regulated environment and get used., • Scale potential: You'll start with a small team of senior engineers and have the chance to grow the function as demand and impact increase., • Broad technical latitude: The team is technology agnostic, so you'll work across multiple model providers, cloud platforms, and modern data tooling.

Take2 has proven experience bridging the intersection of technology and people solutions. As a proven, trusted provider for our Federal and commercial clients, we provide the right solutions, at the right time through trusted partnerships, customized to solve our client’s unique business challenges. Take2 invests time, discipline, and rigor into our technology and people solutions, as well as utilizes our proprietary People Cloud. Whether we are bridging the gap between IT talent and our customers’ business challenges, Take2 will work as a partner to best resolve client needs. Take2 is hiring a Database Developer who is eligible to be sponsored for a Public Trust Clearance. This position is Remote, but it will require you to work East Coast Hours while being located in the United States. Database Developer What You Will Do We are seeking a highly motivated Developer DBA with strong Oracle SQL and PL/SQL expertise, ETL development experience, and cloud data platform knowledge to support application modernization, database development, systems integration, analytics, and operational support activities. • Design, develop, and support Oracle database solutions, including packages, stored procedures, functions, triggers, views, materialized views, and sequences, ensuring high performance, scalability, and maintainability., • Develop, enhance, and optimize SQL and PL/SQL code to support application functionality, reporting, system integrations, and business operations., • Create and maintain database migration scripts, schema updates, release deployment packages, and automated database deployment processes., • Design, build, and support ETL workflows, data transformation processes, validation frameworks, and batch data processing solutions., • Analyze and interpret complex data models, database schemas, enterprise data flows, ERDs, and data mappings to support business and application requirements., • Develop and support cloud-based data integration, reporting, and analytics solutions using AWS technologies such as Amazon Redshift, Amazon S3, Amazon RDS, and related services., • Build and maintain data integration pipelines using Matillion ETL and other enterprise data integration platforms., • Troubleshoot and resolve database issues, data quality concerns, application integration challenges, and performance bottlenecks across environments., • Perform database performance tuning and optimization using execution plans, indexing strategies, partitioning, optimizer statistics, and Oracle diagnostic and monitoring tools., • Collaborate closely with software engineers, architects, DevOps engineers, and business stakeholders to deliver scalable and reliable data solutions., • Participate in Agile development activities, including sprint planning, backlog refinement, daily standups, code reviews, sprint reviews, and retrospectives., • Contribute to DevSecOps and CI/CD initiatives by implementing automated testing, deployment, monitoring, and compliance practices for database solutions., • Create and maintain technical documentation, including database designs, data mappings, interface specifications, operational procedures, and support guides., • Develop and support database unit testing, regression testing, data validation testing, and automated test support for SQL, PL/SQL, ETL, and data migration changes., • Support production operations, release deployments, incident troubleshooting, root-cause analysis, and post-deployment validation for mission-critical database and application components., • Leverage approved AI-powered development tools to accelerate SQL and PL/SQL development, code review, troubleshooting, documentation, testing, and knowledge-sharing activities., • Identify and implement opportunities to apply AI and intelligent automation to database development, data engineering, deployment, monitoring, operational support, and engineering productivity., • Support data governance, security, privacy, and compliance requirements by implementing least-privilege access, auditability, encryption-aware data handling, and controls aligned with Federal standards such as FISMA, NIST, and FedRAMP where applicable., • Continuously evaluate emerging database, cloud, AI, and data engineering technologies and recommend improvements that enhance platform capabilities and team productivity. What You Will Need • Bachelor’s degree required., • Minimum of five (5) years of experience in Oracle database development, database engineering, ETL development, data integration, or related software engineering roles., • Strong experience in Oracle SQL and PL/SQL development, including packages, procedures, functions, triggers, views, materialized views, sequences, complex queries, performance tuning, and Oracle development tools such as SQL Developer and SQL*Loader., • Proven ability to design, develop, optimize, troubleshoot, and maintain database solutions supporting enterprise and mission-critical applications., • Experience designing and implementing ETL solutions, data transformation processes, batch processing frameworks, and data migration initiatives., • Experience with AWS data and cloud services, including Amazon Redshift, Amazon RDS, Amazon S3, and related analytics and integration technologies., • Experience with Matillion ETL or similar cloud-native data integration, orchestration, and data pipeline platforms., • Strong understanding of relational database architecture, logical and physical data modeling, ERDs, normalization, indexing, query optimization, transaction management, and referential integrity., • Experience analyzing and supporting complex enterprise application databases, schemas, and data models., • Experience developing or supporting database unit tests, regression tests, data validation checks, and deployment verification activities., • Experience supporting production releases, operational incidents, defect triage, root-cause analysis, and post-release validation in enterprise or mission-critical environments., • Ability to troubleshoot and resolve database, data quality, integration, and application performance issues across distributed environments., • Experience supporting database and application deployments using CI/CD pipelines, release automation, and DevSecOps best practices., • Experience working in Agile software development environments utilizing Scrum, Kanban, or similar methodologies., • Experience using source control and application lifecycle management tools such as Git, GitHub, Bitbucket, GitLab, or Azure DevOps., • Experience using approved AI-assisted tools or automation practices to support code generation, optimization, testing, troubleshooting, documentation, code reviews, knowledge management, and engineering productivity improvements., • Understanding of Responsible AI principles, AI governance, cybersecurity, data privacy, and secure AI adoption within enterprise or government environments., • Familiarity with AI-enhanced SDLC practices, AI-assisted DevSecOps, intelligent automation, and emerging AI engineering patterns., • Strong analytical, problem-solving, communication, and organizational skills, with the ability to manage competing priorities, work independently or collaboratively, and continuously adapt to emerging database, cloud, and AI technologies. What Would Be Nice To Have • Experience supporting Federal Government or large-scale enterprise applications., • Experience supporting Grants Management, Financial Management, Case Management, Regulatory, or workflow-driven systems., • Experience with Oracle Data Pump, Oracle GoldenGate, Informatica, Talend, ODI, Liquibase, Flyway, or similar data integration and migration tools., • Experience with PostgreSQL, SQL Server, MySQL, or other enterprise database platforms., • Experience supporting cloud modernization, database migration, or application modernization initiatives., • Knowledge of Federal data security and compliance practices, including audit logging, least-privilege access, encryption-aware data handling, and security controls aligned with FISMA, NIST, or FedRAMP requirements., • Experience with data warehousing concepts, dimensional modeling, enterprise reporting, and business intelligence platforms such as Power BI, Tableau, or Amazon QuickSight., • Experience supporting data pipelines and data platforms used for analytics, reporting, AI, and machine learning workloads., • Familiarity with AWS cloud-native application and service deployment architectures., • Oracle certifications preferred, including:, • Oracle Certified Professional (OCP), • Oracle Database Administrator Certified Professional, • Oracle SQL Certified Associate, • Experience evaluating and adopting emerging AI technologies to improve software engineering productivity, database development, operational efficiency, and modernization initiatives., • Familiarity with modern AI data architectures, vector databases, retrieval-augmented generation (RAG), AI agents, knowledge management systems, and enterprise AI platforms., • Experience participating in AI-driven modernization efforts, intelligent automation initiatives, or enterprise data and analytics programs., • Hands-on experience with AI-powered development tools such as GitHub Copilot, Microsoft Copilot, Amazon Q Developer, Cursor, ChatGPT Enterprise, Claude, Gemini, or similar platforms.

Unleash your expertise in product development and optimization by leveraging user research, analyzing metrics, and collaborating across one of the world's most innovative financial organizations. Build and scale agentic workflows on the Data & Analytics Agent Platform, composing agents on top of existing platform APIs and driving the creation of new APIs with platform teams where capability gaps exist. The role is hands-on and delivery-led: translating data management and data governance processes into working agents, tools, and orchestrations rather than documentation. Data management and governance expertise is applied as domain knowledge that shapes what gets built the primary measure of success is agents in production that materially reduce manual effort. Act as the single accountable owner for data management and data governance across the Data & Analytics Agent. As a Senior Product Associate in Data and Analytics Team, you contribute to the team by leveraging your expertise in product development and optimization to make a significant impact, supported by user research and customer feedback to fuel the creation of innovative products and continuously improve existing offerings. Collaborate closely with cross-functional teams and play a crucial role in shaping the future of our products and ongoing success. Builder's instinct with a strong bias to shipping; pragmatic judgement on where agents add value and where deterministic automation is the better answer; clear technical writing for both engineering and control audiences; resilience in ambiguity as the technology and the platform evolve; collaborative influence without direct authority. Success Measures (first 12 months), A defined portfolio of agentic workflows delivered to production, with measured reduction in manual governance effort. Reusable agent patterns and tool library adopted by at least one team beyond your own. New or extended platform APIs delivered jointly with platform teams, unblocking previously infeasible workflows. Evaluation and monitoring in place for every production agent, with published reliability baselines and trends. No material data, privacy, or access incidents attributable to agents you built; governance approvals obtained without extended remediation cycles. Job Responsibilities • Partners with the Product Manager to identify new product opportunities that reflect the needs of our customers and the market through user research and discovery, • Considers and plans for upstream and downstream implications of new product features on the overall product experience, • Supports the collection of user research, journey mapping, and market analysis to inform the strategic product roadmap and provide insight on potential product features that provide value to customers, • Analyzes, tracks, and evaluates product metrics including work to time, cost, and quality targets across the product development life cycle, • Writes the requirements, epics, and user stories to support product development, • Serve as the DMDG platform's primary person to work on the Data & Analytics Agentic Platform. Be part of the team to design, build, test, and ship agentic workflows that automate data management and governance activities metadata capture, classification, lineage reconstruction, access-request triage, data quality triage, issue routing, control evidence collection, and policy question answering., • Decompose multi-step governance processes into agent-executable task graphs: tool selection, prompt and instruction design, context and retrieval strategy, memory, deterministic versus LLM step boundaries, human-in-the-loop checkpoints, and fallback behavior. Own the full agent lifecycle: prototype, evaluate, harden, release, monitor, and iterate. Define evaluation sets and quality baselines before scaling, and instrument agents for cost, latency, accuracy, and intervention rate. Build reusable patterns, tool libraries, and reference implementations so other teams can compose agents without re-solving the same problems., • Map existing platform APIs to target workflows; compose agents and tools over them, handling authentication, entitlements propagation, pagination, rate limits, idempotency, and error recovery. Identify capability gaps and partner with platform engineering to co-develop new APIs: write the use-case brief, propose contracts and schemas, specify payloads and semantics, agree SLAs, and validate against real agent workloads. Act as design partner and first consumer for new platform APIs pilot early versions, feedback on ergonomics and performance, and drive toward stable, agent-friendly interfaces. Where no API exists and none is near-term viable, define the interim path and the conditions for retiring it, avoiding durable workarounds., • Run an opportunity pipeline with data office and business stakeholders: identify candidate workflows, size effort and benefit, prioritize, and sequence delivery. Work in close partnership with Area Product Owners and engineering to land agent work in the platform roadmap and release cycles. Lead or coach a small team of engineers and solution builders; set technical direction and raise the bar on agent build quality. Report delivery and impact metrics to senior management: workflows automated, hours displaced, adoption, agent reliability, and cost to serve., • Apply working knowledge of data management and governance to ensure agents respect classification, entitlements, retention, cross-border constraints, and approved-use boundaries by design and never widen source-system permissions. Navigate the firm's AI/ML and data governance review requirements efficiently, preparing the artefacts needed to get agents approved and into production. Ensure agent actions, inputs, and outputs are logged and traceable to an acceptable standard for audit and control testing. Required Qualifications, Capabilities, and Skills • 3+ years of experience or equivalent expertise in product management or a relevant domain area, • Proficient knowledge of the product development life cycle, • Experience in product life cycle activities including discovery and requirements definition, • Developing knowledge of data analytics and data literacy, • 3+ years in engineering, data platform, or technical solution delivery, with recent hands-on experience building LLM based or agentic applications in production, not solely prototypes or proofs of concept., • Demonstrated ability to build against and integrate with enterprise APIs; strong practical grasp of REST and event-driven patterns, authentication and authorization models, and integration failure modes., • Proficiency in Python (or equivalent) and current agent frameworks and orchestration tooling; comfortable with RAG patterns, vector stores, tool and function calling, and multi agent or multi-step orchestration., • Experience designing evaluation and monitoring for non-deterministic systems offline evals, regression testing, guardrails, and production observability., • Proven record influencing platforms and engineering teams to build or change APIs, including writing clear technical requirements and contracts., • Working knowledge of data management and data governance concepts, metadata, lineage, classification, data quality, entitlements, retention, sufficient to automate those processes credibly and engage governance stakeholders as a peer. Preferred Qualifications, Capabilities, and Skills • Prior work automating risk, control, or governance processes in a regulated environment., • Experience in financial services, or another industry with comparable data protection and auditability constraints., • Exposure to cloud data platforms, Lakehouse architectures, and modern data tooling. Familiarity with AI governance expectations and applicable data protection regulation, from the perspective of getting systems approved and deployed. Experience leading small technical teams or acting as tech lead on multi workstream delivery.