Senior Data Scientist

We are hiring a Senior Data Scientist with deep expertise in AI agent architectures, large language models (LLMs), and natural language processing (NLP).

This role is critical in developing interoperable, context-aware, and self-improving agents that operate across clinical, administrative, and benefits platforms in the healthcare domain.

• Design and implement Agent-to-Agent (A2A) protocols for autonomous collaboration, negotiation, and task delegation between specialized AI agents (e.g., Claims Agent, Eligibility Agent, Provider Match Agent ).
• Architect and operationalize Model Context Protocol (MCP) pipelines to support persistent, memory-augmented, and contextually grounded LLM interactions.
• Build intelligent multi-agent systems orchestrated by LLM-driven planning modules for 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 document understanding, intent classification, and personalized recommendations.
• Develop retrieval-augmented generation (RAG) systems and structured context libraries for dynamic knowledge grounding using structured (FHIR/ICD-10) and unstructured sources (EHR notes, chat logs).
• Collaborate with engineers and data architects to build scalable, secure, and explainable agentic pipelines 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.
• Contribute to production deployment through robust MLOps pipelines for versioning, monitoring, and continuous model improvement.

• 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.
• Hands‑on experience with Agent-to‑Agent protocols and multi‑agent orchestration tools (e.g., Lang Graph, Auto Gen, CrewAI).
• Practical experience implementing Model Context Protocols (MCP) for modular agent interactions and conversational memory.
• Strong coding skills in Python and proficiency with ML/NLP libraries such as Hugging Face Transformers, PyTorch, Lang Chain, spaCy.
• Familiarity with healthcare benefit systems, including plan structures, claims data, and eligibility rules.
• Experience with healthcare data standards (FHIR, HL7, ICD/CPT, X12 EDI).
• Cloud‑native development experience on AWS, Azure, or GCP, including Kubernetes, Docker, and CI/CD.

• Deep understanding of MCP and Vector
  
  DB integration for dynamic agent memory and retrieval.
• Prior experience deploying LLM‑based agents in production or large‑scale healthcare operations.
• Experience with voice AI, automated care navigation, or AI triage tools.
• Published research or patents in agent systems, LLM architectures, or contextual AI frameworks.