Databricks MLOps Engineer

MLOps and LLMOps Engineer

Job Summary

The MLOps and LLMOps Engineer owns the path from experiment to production and everything that happens after. This role builds the pipelines, registries, monitoring, and release process that let data science teams ship models repeatedly and safely. It applies the same discipline to generative AI systems, where prompts, retrieval indexes, and model versions all need to be tracked and rolled back like any other release artifact.

Key Responsibilities

• Build automated training, validation, and deployment pipelines for machine learning and generative AI workloads
• Operate the MLflow model registry with clear promotion gates across development, staging, and production
• Implement feature engineering pipelines and manage features through Databricks Feature Store
• Build monitoring for data drift, prediction drift, model performance decay, and endpoint health
• Version and manage prompts, retrieval indexes, and evaluation datasets alongside model artifacts
• Define rollback and canary release strategies for model endpoints
• Automate cost and latency reporting for inference workloads
• Partner with data science teams to make the production path the easy path

Required Skills

• 5+ years in MLOps, DevOps, or data engineering, including production model deployment
• Strong Python and hands on MLflow experience across tracking, projects, models, and registry
• CI/CD pipeline construction using GitHub Actions, Azure DevOps, GitLab, or equivalent
• Experience with model serving infrastructure and endpoint monitoring
• Understanding of the machine learning lifecycle end to end, including retraining triggers
• Infrastructure as code experience, preferably Terraform

Preferred Skills

• Databricks Certified Machine Learning Professional
• Experience with Databricks Asset Bundles for ML project deployment
• LLMOps specific experience covering prompt versioning, evaluation pipelines, and inference cost control
• Kubernetes and container orchestration experience
• Model risk management or regulatory model documentation experience

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