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Role OverviewMercor is collaborating with a global insurance and financial services client to support actuarial modeling processes via high-quality data preparation. We are seeking actuarial professionals with experience in seriatim data extraction, cleansing, and validation from life insurance policy administration systems. This opportunity centers around ensuring reliable, audit-ready datasets for valuation, modeling, and financial reporting workflows. This is a short-term, project-based opportunity with high autonomy and clearly scoped deliverables. Key Responsibilities - Extract policy-level data for a given valuation date from systems such as FAST, OIPA, or Sapiens. - Validate record counts against control totals and identify missing or inconsistent fields. - Reconcile in-force movements between periods, including new issues and terminations. - Identify and flag policies with anomalous data for manual review. - Map product codes to actuarial model inputs and document data assumptions. - Deliver a clean, formatted dataset suitable for actuarial projection software. Ideal Qualifications - 5+ years of experience in actuarial data processing or valuation support. - Familiarity with life insurance policy administration systems (e.g., OIPA, FAST, LIFE/400). - Proficiency with Excel and data quality tools; basic scripting knowledge (SQL, Python) is a plus. - Strong attention to detail and understanding of actuarial data structures. - Ability to clearly document data anomalies and reconciliation logic. More About the Opportunity - Remote and asynchronous — control your own work schedule - Expected commitment: min 30 hours/week - Project duration: ~6 weeks Compensation & Contract Terms - $75–100/hour - Independent contractor arrangement - Paid weekly via Stripe Connect Application Process - Submit your resume followed by domain expertise interview and short form About Mercor - Mercor is a talent marketplace that connects top experts with leading AI labs and research organizations. - Our investors include Benchmark, General Catalyst, Adam D’Angelo, Larry Summers, and Jack Dorsey. - Thousands of professionals across domains like law, creatives, engineering, and research have joined Mercor to work on frontier projects shaping the next era of AI.

Job DescriptionCompany Description Vichara is a Financial Services focused products and services firm headquartered in NY and building systems for some of the largest i-banks and hedge funds in the world. Job Description Key Responsibilities Architecture & System Design * Architect, design, and lead multi-agent LLM systems using LangGraph, LangChain, and Promptfoo for prompt lifecycle management and benchmarking. * Build Retrieval-Augmented Generation (RAG) pipelines leveraging hybrid vector search (dense + keyword) using LanceDB, Pinecone, or Elasticsearch. * Define system workflows for summarization, query routing, retrieval, and response generation, ensuring minimal latency and high precision. * Develop RAG evaluation frameworks combining retrieval precision/recall, hallucination detection, and latency metrics — aligned with analyst and business use cases. AI Model Integration & Fine-Tuning * Integrate GPT-4o, PaLM 2, and open-weight models (LLaMA, Mistral) for task-specific contextual Q&A. * Fine-tune transformer models (BERT, SentenceTransformers) for document classification, summarization, and sentiment analysis. * Manage prompt routing and variant testing using Promptfoo or equivalent tools. Agentic AI & Orchestration * Implement multi-agent architectures with modular flows — enabling task-specific agents for summarization, retrieval, classification, and reasoning. * Design fallback and recovery behaviors to ensure robustness in production. * Employ LangGraph for parallel and stateful agent orchestration, error recovery, and deterministic flow control. Data Engineering & RAG Infrastructure * Architect ingestion pipelines for structured and unstructured data — including financial statements, filings, and PDF documents. * Leverage MongoDB for metadata storage and Redis Streams for async task execution and caching. * Implement vector-based search and retrieval layers for high-throughput and low-latency AI systems. Observability & Production Deployment * Deploy end-to-end AI systems on AWS EKS / Azure Kubernetes Service, integrated with CI/CD pipelines (Azure DevOps). * Build comprehensive monitoring dashboards using OpenTelemetry and Signoz, tracking latency, retrieval precision, and application health. * Enforce testing and regression validation using golden datasets and structured assertion checks for all LLM responses. Cross-functional Collaboration * Collaborate with DevOps, MLOps, and application development teams to integrate AI APIs with React / FastAPI-based user interfaces. * Work with business analysts to translate credit, compliance, and customer-support requirements into actionable AI agent workflows. * Mentor a small team of GenAI developers and data engineers in RAG, embeddings, and orchestration techniques. Qualifications • Experience:, • 5+ years as an AI or ML Engineer Required Skills & Experience * LLMs & GenAI: GPT-4o, PaLM 2, LangGraph, LangChain, Promptfoo, SentenceTransformers * RAG Frameworks: LanceDB, Pinecone, ElasticSearch, FAISS, MongoDB * Agentic AI: LangGraph multi-agent orchestration, routing logic, task decomposition * Fine-Tuning: BERT / domain-specific transformer tuning, evaluation framework design * Infra & MLOps: FastAPI, Docker, Kubernetes (EKS/AKS), Redis Streams, Azure DevOps CI/CD * Monitoring: OpenTelemetry, Signoz, Prometheus * Languages & Tools: Python, SQL, REST APIs, Git, Pandas, NumPy * Nice-to-Have Skills * Knowledge of Reranker-based retrieval (MiniLM / CrossEncoder) * Familiarity with Prompt evaluation and scoring (BLEU, ROUGE, Faithfulness) * Domain exposure to Credit Risk, Banking, and Investment Analytics * Experience with RAG benchmark automation and model evaluation dashboards Additional Information