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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
We are looking for a Machine Learning Engineer to build and support production-ready AI systems in Los Angeles, California. This position focuses on creating reliable machine learning infrastructure, enabling scalable model operations, and advancing generative AI solutions that improve access to business-critical information. The ideal candidate will combine strong platform engineering skills with hands-on experience deploying models, automating workflows, and maintaining high standards for quality, governance, and performance.
Responsibilities:
- Build and maintain scalable machine learning infrastructure in Databricks, including experiment tracking, model management, and serving capabilities for production use.
- Create and improve MLOps frameworks and automated deployment pipelines that support model release, monitoring, and lifecycle management.
- Establish disciplined processes for model version control, retraining, and artifact governance using tools such as Unity Catalog.
- Develop and administer a feature store strategy that keeps training and inference data consistent across machine learning workflows.
- Design retrieval-augmented generation solutions that allow internal teams to search and interact with documents such as fund materials, investor communications, and research content.
- Implement and manage vector search platforms to support semantic retrieval across large collections of enterprise documents.
- Customize and fine-tune large language models using proprietary datasets while protecting data privacy and meeting compliance expectations.
- Build document ingestion and transformation pipelines that handle parsing, segmentation, and embedding creation for generative AI applications.
- Introduce prompt design standards, evaluation methods, and application safeguards to improve response quality, reduce hallucinations, and provide source-backed outputs.
- Automate training, testing, orchestration, and deployment workflows through CI/CD pipelines and tools such as Databricks Workflows, GitHub Actions, Azure DevOps, Airflow, or Prefect.