Description

You will build and deploy context-aware AI agents for clinical and administrative healthcare workflows.

This role is on-site.

Responsibilities

• Develop Agent-to-Agent (A2A) protocols for autonomous collaboration and task delegation.
• Architect Model Context Protocols (MCP) pipelines for persistent memory and contextual grounding.
• Build LLM-driven planning systems to optimize benefit processing and clinical summarization.
• Implement Retrieval-Augmented Generation (RAG) systems integrating structured (FHIR, ICD-10) and unstructured data.
• Deploy secure, HIPAA-compliant MLOps pipelines for production AI systems.

Required Skills

• 7+ years applied AI experience, focused on LLMs, transformers, or NLP in healthcare.
• Hands-on experience with multi-agent orchestration: LangGraph, AutoGen, or CrewAI.
• Practical knowledge of Model Context Protocols (MCP) for long-lived conversational memory.
• Strong proficiency in Python and ML/NLP libraries: Hugging Face Transformers, PyTorch, LangChain, spaCy.
• Experience fine-tuning and deploying domain-specific LLMs for document understanding and intent classification.
• Familiarity with healthcare data standards: FHIR and ICD-10.

Key Skills

Education

Any Graduate