Description

You will design and implement AI agent architectures and multi-agent systems for healthcare applications.

Responsibilities

• Design A2A protocols for autonomous collaboration and task delegation between specialized AI agents.
• Architect MCP pipelines to enable persistent, memory-augmented, and contextually grounded LLM interactions.
• Build multi-agent systems orchestrated by LLM-driven planning modules for benefit processing and clinical summarization.
• Fine-tune domain-specific LLMs and NLP models for document understanding and intent classification.
• Develop retrieval-augmented generation (RAG) systems using structured and unstructured healthcare data.
• Lead research in memory-based agent systems, RLHF, and context-aware task planning.

Required Skills

• 7+ years of experience in applied AI focusing on LLMs, transformers, or NLP in healthcare.
• Master’s or Ph.D. in Computer Science, Machine Learning, or Computational Linguistics.
• Hands-on experience with A2A protocols and orchestration tools like LangGraph, AutoGen, or CrewAI.
• Practical implementation of MCP for long-lived conversational memory.
• Proficiency in Python and libraries including Hugging Face Transformers, PyTorch, LangChain, and spaCy.
• Experience with healthcare data standards such as FHIR, HL7, ICD/CPT, and X12 EDI.
• Cloud-native development on AWS, Azure, or GCP using Kubernetes and Docker.
• Knowledge of CI/CD and MLOps pipelines for model versioning and monitoring.

Preferred Skills

• Deep understanding of MCP and VectorDB integration for dynamic agent memory.
• Experience deploying LLM-based agents in production healthcare systems.

Key Skills

Education

Master’s or Ph.D. in Computer Science