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Machine Learning Engineer
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Robert Half
Los Angeles, CA (In Person)
Full-Time
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Job Description
Description
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.
- Design and manage Databricks Feature Store for consistent feature engineering across training and inference pipelines.
AI & LLM
Operations- 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.
- Build guardrails and safety measures for GenAI applications, including hallucination detection, output validation, and source attribution. Automation & CI/CD Pipelines
- 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.
- Automate complex data and model workflows utilizing orchestration tools such as Airflow, Prefect, or Databricks Workflows.