Machine Learning Engineer

Robert Half is seeking a Machine Learning Engineer to design, deploy, and operate scalable ML infrastructure in a Databricks-centric environment. The role emphasizes GenAI and LLM systems, MLOps platforms, feature stores, vector search, and retrieval augmented generation, with on-site work in Los Angeles and a compensation range of $200,000 - $260,000 per year.

The position focuses on building and maintaining robust ML pipelines and governance, enabling production-grade model deployment, monitoring, and optimization while facilitating advanced document understanding and enterprise search capabilities.

Responsibilities

• Lead the architecture, deployment, and ongoing upkeep of scalable ML infrastructure on Databricks, including MLflow for experiment tracking, a model registry, and endpoints for model serving.
• Oversee the ML Ops platform and automated pipelines that deploy, monitor, and sustain models in production.
• Implement solid solutions for model versioning, systematic retraining, and artifact management using Databricks Unity Catalog for governance.
• Design and manage the Databricks Feature Store to ensure consistent feature engineering across training and inference workflows.
• Architect and deploy Retrieval-Augmented Generation systems for document Q&A to support queries of fund documents, investor letters, and market research.
• Design, deploy, and manage vector database solutions (Databricks Vector Search, Pinecone, or similar) to enable semantic search across enterprise documents.
• Lead fine-tuning and customization of LLMs, including Claude or open-source models, using CIM proprietary data while maintaining privacy and compliance.
• Develop and optimize document processing pipelines (PDF parsing, chunking strategies, embedding generation) for RAG applications.
• Implement prompt engineering best practices and establish 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.
• Design and implement extensive automation across the ML lifecycle, covering training, testing, validation, and deployment with Databricks Workflows and Asset Bundles.
• Set up robust CI/CD pipelines for both traditional ML models and GenAI applications using GitHub Actions, Azure DevOps, or comparable tools.
• Automate complex data and model workflows leveraging orchestration tools such as Airflow, Prefect, or Databricks Workflows.

Technologies

• Databricks
• MLflow
• Databricks Unity Catalog
• Databricks Feature Store
• Databricks Vector Search
• Pinecone
• Claude
• GitHub Actions
• Azure DevOps
• Airflow
• Prefect
• Databricks Workflows
• Asset Bundles
• Python
• TensorFlow

Benefits

• Medical insurance
• Vision insurance
• Dental insurance
• Life insurance
• Disability insurance
• 401(k) plan
• Free online training