Senior Data Scientist

We are hiring a Senior Data Scientist with deep expertise in AI agent architectures, large language models (LLMs), natural language processing (NLP), and hands-on experience with Agent-to-Agent (A2A) Protocols and Model Context Protocols (MCP). 

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

Key Responsibilities

• Design and implement A2A protocols for autonomous collaboration, negotiation, and task delegation between specialized AI agents (e.g., ClaimsAgent, EligibilityAgent, ProviderMatchAgent).
• Architect and operationalize MCP pipelines to enable 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 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 across structured (FHIR/ICD-10) and unstructured sources (EHR notes, chat logs).
• Collaborate with engineers and data architects to build scalable, secure, and compliant agentic pipelines.
• 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 model versioning, monitoring, and continuous improvement.

Required Qualifications

• 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 A2A protocols and multi-agent orchestration tools such as LangGraph, AutoGen, or CrewAI.
• Practical experience implementing MCP for long-lived conversational memory and modular agent interactions.
• Strong programming skills in Python and proficiency with ML/NLP libraries such as Hugging Face Transformers, PyTorch, LangChain, and spaCy.
• Familiarity with healthcare benefit systems, including plan structures, claims data, and eligibility rules.
• Experience with healthcare data standards such as FHIR, HL7, ICD/CPT, and X12 EDI formats.
• Cloud-native development experience on AWS, Azure, or GCP, including Kubernetes, Docker, and CI/CD.

Preferred Qualifications

• Deep understanding of MCP and VectorDB integration for dynamic agent memory and retrieval.
• Experience deploying LLM-based agents in production healthcare systems.
• Exposure to voice AI, automated care navigation, or AI triage tools.
• Published research or patents in agent systems, LLM architectures, or contextual AI frameworks