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Tier-1 brand plus mid-senior ML title, but specialized LLM/Databricks reduces applicant density.
LLM and MLOps skills are transferable, but healthcare governance and Databricks specialization increase domain specificity.
Multiple mandatory production LLM, Databricks, Azure, MLOps, and governance requirements enforce strict screening.
Design, build, and productionize LLM-based agents and AI/ML pipelines on Databricks for healthcare data, focusing on Role-based Provider 360, Patient 360, Clinic 360 surfaces.
Own model evaluation, prompt versioning, and deploy/monitor models in production on Azure with CI/CD including model, data, and prompt versioning.
Partner with governance bodies to manage risk, support pilot rollouts, and communicate technical tradeoffs to executive stakeholders including CIO-level presentations.
Graduate degree or equivalent experience.
Hands-on experience building and deploying LLM-based agents with multi-tool orchestration, including RAG and vector search systems.
Proven solid production-grade Python and SQL skills with Databricks fluency (Delta Lake, Unity Catalog, Genie spaces, AI functions).
Experience in cloud model deployment and monitoring on Azure, including CI/CD for ML pipelines with data and prompt versioning.
Experienced in governed healthcare data environments, particularly with data-product/Gol-layer design and compliance.
Strong operational focus on systematic testing, versioning, and productionizing AI/ML models, especially LLM prompt engineering and evaluation.
Capable of translating complex AI/ML technical trade-offs to non-technical and executive audiences, demonstrating governance-minded judgment and risk awareness.