Description

You will build interoperable, context-aware, and self-improving AI agents for healthcare platforms.

This role is on-site.

Responsibilities

• Design and implement Agent-to-Agent (A2A) protocols for autonomous collaboration and task delegation.
• Architect and operationalize Model Context Protocol (MCP) pipelines for grounded LLM interactions across multi-turn use cases.
• Build multi-agent systems orchestrated by LLM-driven planning modules to streamline benefit processing and summarization.
• Develop RAG systems and structured context libraries to ground knowledge across FHIR/ICD-10 and unstructured sources.

Required Skills

• 7+ years of experience in applied AI focusing on LLMs, transformers, or NLP within healthcare.
• Master’s or Ph.D. in Computer Science, Machine Learning, or Computational Linguistics.
• Hands-on experience with Agent-to-Agent protocols, LangGraph, AutoGen, or CrewAI.
• Practical implementation experience of Model Context Protocols (MCP) for conversational memory.
• Strong Python coding skills with proficiency in PyTorch, Hugging Face Transformers, LangChain, and spaCy.
• Familiarity with healthcare data standards (FHIR, HL7, ICD/CPT, X12 EDI).
• Cloud-native development experience on GCP, including Kubernetes and Docker.

Key Skills

Education

Any Graduate