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1 result for Ai Architect in Cerritos, CA

Machine Learning Engineer

Los Angeles, CA
onsite
Permanent / Full Time
200000.00 - 260000.00 USD / Yearly
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. Generative 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.
2026-06-11T14:08:43Z