Description
The MLOps and LLMOps Engineer owns the production path for machine learning and generative AI systems, building automated training, validation, and deployment pipelines; operating MLflow registries; managing features through Databricks Feature Store; implementing drift and endpoint monitoring; versioning prompts and retrieval indexes; and defining rollback and canary release strategies. The role requires at least five years of experience in MLOps, DevOps, or data engineering, strong Python and MLflow skills, CI/CD experience, model-serving and monitoring knowledge, and infrastructure-as-code experience.
