Compunnel, Inc.
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. Email ID * This field is required Please enter valid emailId. Cell phone * This field is required Please enter valid cell phone. First Name * This field is required Please enter valid first name. Last Name * This field is required Please enter valid last name.
#J-18808-Ljbffr
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. Email ID * This field is required Please enter valid emailId. Cell phone * This field is required Please enter valid cell phone. First Name * This field is required Please enter valid first name. Last Name * This field is required Please enter valid last name.
#J-18808-Ljbffr