Senior Data Scientist

Description

Onsite Requirement - onsite need technically strong candidates

• AI agent architectures, LLMs, NLP developing A2A Protocols and Model Context Protocols (MCP)
• LLMs and NLP models (e.g., medical BERT, BioGPT)
• retrieval-augmented generation (RAG)
• coding experience in Python, with proficiency in ML/NLP libraries
• healthcare data standards like FHIR, HL7, ICD/CPT, X12 EDI formats.
• AWS, Azure, or GCP including Kubernetes, Docker, and CI/CD

Description

• We are hiring a Senior Data Scientist with deep expertise in AI agent architectures, LLMs, NLP, and hands-on development experience with A2A Protocols and Model Context Protocols (MCP).
• This role is integral in building interoperable, context-aware, and self-improving agents that interact across clinical, administrative, and benefits platforms.

Key Responsibilities

• Design and implement Agent-to-Agent (A2A) protocols enabling 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 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.

Required Qualifications

• 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, Lang Chain, 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.