Description

You will build interoperable, context-aware, and self-improving AI agents operating across clinical, administrative, and benefits platforms.

Responsibilities

• Develop and implement Agent-to-Agent (A2A) protocols for autonomous agent collaboration and task delegation.
• Architect and operationalize Model Context Protocols (MCP) pipelines for persistent memory and contextual grounding in healthcare.
• Build intelligent systems orchestrated by LLM-driven planning modules to optimize workflows like benefit processing and clinical summarization.
• Develop Retrieval-Augmented Generation (RAG) systems to integrate structured (FHIR, ICD-10) and unstructured data.
• Contribute to production deployment via MLOps pipelines ensuring pipelines are secure and compliant with regulations like HIPAA.

Required Skills

• 7+ years of experience in applied AI, focused on LLMs, transformers, agent frameworks, or NLP within healthcare.
• Hands-on experience with A2A protocols, LangGraph, AutoGen, CrewAI, or similar multi-agent orchestration tools.
• Practical knowledge of Model Context Protocols (MCP) for long-lived conversational memory.
• Strong proficiency in Python and ML/NLP libraries including Hugging Face Transformers, PyTorch, LangChain, and spaCy.
• Experience fine-tuning and deploying domain-specific LLMs for document understanding and intent classification.
• Familiarity with integrating structured data standards like FHIR and ICD-10.

Key Skills

Education

Any Graduate