The company is looking to solve the problem of building interoperable, context-aware, and self-improving agents that interact across clinical, administrative, and benefits platforms in the healthcare domain.
Requirements
- Hands-on experience with Agent-to-Agent protocols, LangGraph, AutoGen, CrewAI, or similar multi-agent orchestration tools.
- Practical knowledge and implementation experience of Model Context Protocols (MCP) for long-lived conversational memory and modular agent interactions.
- Strong coding experience in Python, with proficiency in ML/NLP libraries like Hugging Face Transformers, PyTorch, LangChain, spaCy, etc.
- Familiarity with healthcare benefit systems, including plan structures, claims data, and eligibility rules.
- Experience with healthcare data standards like FHIR, HL7, ICD/CPT, X12 EDI formats.
- Cloud-native development experience on AWS, Azure, or GCP including Kubernetes, Docker, and CI/CD.
- Deep understanding of MCP + VectorDB integration for dynamic agent memory and retrieval.
Responsibilities
- Design and implement Agent-to-Agent (A2A) protocols enabling autonomous collaboration, negotiation, and task delegation between specialized AI agents (e.g., ClaimsAgent, EligibilityAgent, ProviderMatchAgent).
- Architect and operationalize Model Context Protocol (MCP) pipelines that ensure persistent, memory-augmented, and contextually grounded LLM interactions across multi-turn healthcare use cases.
- Build intelligent multi-agent systems orchestrated by LLM-driven planning modules to streamline benefit processing, prior authorization, clinical summarization, and member engagement.
- Fine-tune and integrate domain-specific LLMs and NLP models (e.g., medical BERT, BioGPT) for complex document understanding, intent classification, and personalized plan recommendations.
- Develop retrieval-augmented generation (RAG) systems and structured context libraries to enable dynamic knowledge grounding across structured (FHIR/ICD-10) and unstructured sources (EHR notes, chat logs).
- Collaborate with engineers and data architects to build scalable agentic pipelines that are secure, explainable, and compliant with healthcare regulations (HIPAA, CMS, NCQA).
- Lead research and prototyping in memory-based agent systems, reinforcement learning with human feedback (RLHF), and context-aware task planning.
Other
- Master's or Ph.D. in Computer Science, Machine Learning, Computational Linguistics, or a related field.
- 7+ years of experience in applied AI with a focus on LLMs, transformers, agent frameworks, or NLP in healthcare.
- Need to be local for F2F interview in Woodland Hills, CA with upto 1.5 Hrs Commute
- Published research or patents in agent systems, LLM architectures, or contextual AI frameworks.
- Prior work on LLM-based agents in production systems or large-scale healthcare operations.
