Sr. Machine Learning Ops Engineer (Director)
7 days ago
Los Angeles
Job DescriptionABOUT CIM GROUP:CIM is a community-focused real estate and infrastructure owner, operator, lender, and developer. Our team of experts works together to identify and create value in real assets, benefiting the communities in which we invest. Back in 1994, our three founders focused on projects in Southern California neighborhoods. Today, we are a diverse team of 900+ employees with projects across the Americas. Our projects have delivered jobs; created comfortable places to live, work, and relax; and provided necessary and sustainable infrastructure. Our focus on enhancing communities is unwavering, and we strive to make an even greater impact in the years to come. Join us and make an impact today! POSITION PURPOSE: The Senior ML Ops Engineer leads the design and maintenance of scalable, secure infrastructure for ML model deployment, lifecycle management, and Generative AI enablement. This role is responsible for building and operating the firm's ML Ops platform on Databricks, with a strategic focus on productionizing GenAI/LLM solutions including Retrieval-Augmented Generation (RAG) systems and vector database implementations. The Senior ML Ops Engineer ensures models transition from development to production while meeting regulatory and compliance standards. This role collaborates closely with Data Science, Platform Engineering, Information Architecture, and business vertical teams (Fund Accounting, Investor Relations, Investments) to accelerate ML-driven insights, enhance model accuracy, and govern the ML/AI ecosystem. Beyond technical execution, the role defines ML Ops strategy and architecture, addressing the "last mile" challenge of AI value realization by automating and scaling ML models and GenAI applications as tangible business assets. This role serves as the key pillar that enhances efficiency, boosts model accuracy, accelerates time-to-market for AI solutions, and ensures the scalability and robust governance of machine learning and generative AI initiatives. RESPONSIBILITIES: ML Model Deployment & Platform Management • Lead the design, implementation, and ongoing maintenance of scalable ML infrastructure on Databricks, including ML flow for experiment tracking, model registry, and model serving endpoints., • Oversee the development of the ML Ops platform and automated pipelines for deploying, monitoring, and maintaining models within production environments., • Implement robust solutions for model versioning, systematic retraining, and comprehensive artifact management using Databricks Unity Catalog for ML governance., • Architect and implement Retrieval-Augmented Generation (RAG) systems for document Q&A, enabling business teams to query fund documents, investor letters, and market research., • Design, deploy, and manage vector database solutions (Databricks Vector Search, Pinecone, or similar) for semantic search and retrieval across enterprise documents., • Lead LLM fine-tuning and customization initiatives, training models like Claude or open-source alternatives with CIM proprietary data while ensuring data privacy and compliance., • Develop and optimize document processing pipelines including PDF parsing, chunking strategies, and embedding generation for RAG applications., • Implement prompt engineering best practices and LLM evaluation frameworks to ensure output quality, relevance, and factual accuracy., • Design and implement extensive automation across the ML workflow, covering model training, testing, validation, and deployment using Databricks Workflows and Asset Bundles., • Set up robust CI/CD pipelines for both traditional ML models and GenAI applications, leveraging GitHub Actions, Azure DevOps, or similar tools., • Implement comprehensive monitoring and alerting systems for real-time tracking of model performance, data quality, and GenAI output quality., • Utilize specialized tools (Evidently AI, WhyLabs, Prometheus/Grafana) to proactively detect model drift, data quality anomalies, and RAG retrieval degradation., • Develop evaluation frameworks for GenAI applications including relevance scoring, faithfulness metrics, and human feedback loops., • Build and maintain sophisticated feature stores on Databricks, ensuring precise alignment between training and inference data pipelines., • Collaborate with data engineers and information architects to build robust ETL pipelines that feed into the Databricks Lakehouse., • Integrate robust security measures directly into ML Ops and GenAI pipelines, including access controls via Unity Catalog and data encryption., • Implement Trustworthy AI guardrails addressing bias detection, explainability, prompt injection prevention, and responsible AI practices., • Ensure GenAI applications handling sensitive fund and investor data comply with regulatory requirements and internal policies., • Engage in extensive collaboration with data scientists, platform engineers, information architects, and DevOps teams to ensure seamless ML/AI integration., • Partner with business teams (Fund Accounting, FP&A, Investor Relations, Sales, Investments) to identify high-value AI use cases and translate business needs into technical solutions., • Communicate complex AI concepts in business terms, managing expectations and demonstrating ROI of ML/GenAI initiatives., • Bachelor's or Master's degree in Computer Science, Engineering, Information Systems, or a related field., • 7+ years of experience as an ML Ops Engineer, ML Engineer, or similar role with production deployment responsibility., • Expert-level proficiency in Python, complemented by strong skills in Bash scripting., • Extensive experience designing and implementing cloud solutions on Azure (required) or GCP., • Deep expertise with Docker and Kubernetes for containerizing and orchestrating ML workloads., • Hands-on experience with CI/CD tools such as GitHub Actions, Jenkins, GitLab CI, or Azure DevOps., • Strong SQL proficiency and practical experience with Databricks platform., • Demonstrated experience building and deploying RAG (Retrieval-Augmented Generation) systems in production environments., • Hands-on experience with vector databases (Databricks Vector Search, Pinecone, Weaviate, Chroma, or Milvus)., • Experience with LLM APIs and frameworks (OpenAI, Anthropic Claude, LangChain, LlamaIndex)., • Understanding of embedding models, chunking strategies, and retrieval optimization techniques., • Experience with ML flow for experiment tracking, model registry, and model serving., • Familiarity with Databricks Feature Store and Unity Catalog for ML governance., • Understanding of Delta Lake and Lakehouse architecture for ML data pipelines., • Experience with LLM fine-tuning techniques (LoRA, QLoRA, full fine-tuning) on proprietary data., • Familiarity with ML frameworks including TensorFlow, PyTorch, Scikit-learn, XGBoost., • Experience with model serialization (ONNX) and inference optimization., • Prior experience within financial services, fintech, or private equity sectors., • Experience building ML/AI infrastructure from scratch in entrepreneurial environments. Key Competencies: • Entrepreneurial Mindset: Comfortable building from scratch in environments without mature processes; thrives with ambiguity and takes initiative., • Business Partnership: Exceptional ability to translate complex AI/ML concepts for non-technical stakeholders across Fund Accounting, Investor Relations, and Investment teams., • Cross-Functional Collaboration: Proven track record of building relationships and influence without authority across technical and business functions., • Business Value Orientation: Focuses on ROI and business outcomes, not just technical elegance; can prioritize initiatives based on business impact., • Trustworthy AI Champion: Strong awareness of responsible AI practices, including bias mitigation, explainability, security, and compliance requirements., • Continuous Learner: Stays current with rapidly evolving GenAI landscape; actively experiments with new technologies and approaches.WHAT CIM OFFERS:At CIM, we believe our success stems from our collective efforts, and we are committed to providing well-rounded support and resources for our employees. In addition to a competitive compensation plan, CIM offers a comprehensive benefits program for employees to thrive both inside and outside of work. Eligible employees can enjoy a wide range of benefits, including:, • A variety of Medical, dental, and vision benefit plans, • Health Savings Account with a generous employer contribution, • Company paid life and disability insurance, • 401(k) savings plan, with company match, • Comprehensive paid time off, including: vacation days, 10 designated holidays, sick time, and bereavement leave, • Up to 16 hours of volunteer time off, • Up to 16 weeks of Paid Parental Leave, • Ongoing professional development programs, • Wellness program, including monthly and quarterly prizes
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