MLOps

Role Summary :

Design the architecture for deploying scalable, observable, and compliant AI / ML solutions integrated with clinical workflows and hospital data platforms.

Responsibilities :

• Architect end-to-end ML pipelines using Airflow, and MLFlow or Vertex AI
• Build containerized model inference systems deployed on Kubernetes with AppDynamics and observability
• Full ownership of model lifecycle : development, deployment, drift monitoring
• Proficient with Google Cloud Platform Vertex AI, Azure ML, container orchestration (K8s, Docker)
• Design architecture to support GenAI workloads (LLMs, embeddings) using Vertex AI or Azure OpenAI
• Define governance and guardrails for deploying agentic systems in clinical workflows
• Implement MLOps patterns : model versioning, rollback, shadow testing
• Define architecture for RAG (retrieval augmented generation) systems using vector databases (e.g., FAISS, Pinecone)
• Deploy LLM-based agents and secure GenAI pipelines (prompt injection protection, moderation, output fallback)
• Support agentic AI orchestration with frameworks like LangChain, CrewAI

Required Qualifications :

Preferred Qualifications :