Key responsibilities: - Operationalize GenAI and LLM applications, including retrieval-augmented generation (RAG), vector search, prompt engineering, agentic AI, and Model Context Protocol (MCP). - Lead design, development, and deployment of production-grade LLM and ML pipelines (data transformation, feature engineering, training, tuning, serving). - Architect scalable data/AI workflows on Snowflake, Databricks, and Azure ML; integrate AI models with data lakehouse platforms. - Build and maintain API-based AI services (FastAPI, Flask) for secure, scalable model access. - Define and implement CI/CD pipelines for GenAI/ML services (GitHub Actions, Azure DevOps, MLFlow); manage containers with Kubernetes and Docker. - Develop robust MLOps/LLMOps practices: experiment tracking, model versioning, observability, governance. - Mentor ML engineers and data scientists; drive engineering excellence and production-readiness. - Partner with cross-functional teams to embed AI services into products while ensuring security, compliance, and resilience in regulated healthcare environments. - Troubleshoot production AI systems, address latency, drift, and performance issues

Key responsibilities: - Operationalize GenAI and LLM applications, including retrieval-augmented generation (RAG), vector search, prompt engineering, agentic AI, and Model Context Protocol (MCP). - Lead design, development, and deployment of production-grade LLM and ML pipelines (data transformation, feature engineering, training, tuning, serving). - Architect scalable data/AI workflows on Snowflake, Databricks, and Azure ML; integrate AI models with data lakehouse platforms. - Build and maintain API-based AI services (FastAPI, Flask) for secure, scalable model access. - Define and implement CI/CD pipelines for GenAI/ML services (GitHub Actions, Azure DevOps, MLFlow); manage containers with Kubernetes and Docker. - Develop robust MLOps/LLMOps practices: experiment tracking, model versioning, observability, governance. - Mentor ML engineers and data scientists; drive engineering excellence and production-readiness. - Partner with cross-functional teams to embed AI services into products while ensuring security, compliance, and resilience in regulated healthcare environments. - Troubleshoot production AI systems, address latency, drift, and performance issues