Data Scientist

Key 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.

* Contribute to production deployment through robust MLOps pipelines for versioning, monitoring, and continuous model 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 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 Google Cloud Platform including Kubernetes, Docker, and CI/CD.

Preferred Qualifications

* Deep understanding of MCP + VectorDB integration for dynamic agent memory and retrieval.

* Prior work on LLM-based agents in production systems 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.