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.

• 5+ years of experience in machine learning engineering, MLOps, or a closely related technical role. • Strong proficiency in Python and practical experience building machine learning solutions in production environments. • Hands-on expertise with Databricks and familiarity with capabilities such as experiment tracking, model governance, workflows, and feature management. • Experience deploying and maintaining generative AI or large language model applications, including retrieval-based architectures and vector databases. • Solid understanding of automation, CI/CD practices, and workflow orchestration for data and model pipelines. • Background working with machine learning frameworks such as TensorFlow and applying AI techniques in real-world business settings. • Knowledge of model evaluation, monitoring, and governance practices with attention to reliability, compliance, and traceability. • Experience with computer vision, detection models, or related AI domains is considered valuable.

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Los Angeles, CA
onsite
Permanent / Full Time
180000 - 210000 USD / Yearly
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.
2026-06-11T00:00:00Z