In Person is must - Hybrid- Senior Data Scientist-.-Mason OH-C2C- CH

Overview

Hybrid
$65 - $70
Accepts corp to corp applications
Contract - W2
Contract - 12 Month(s)

Skills

Data Scientist
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

Job Details

Final interview will be in person interview

Role- Senior Data Scientist

Location- Mason, OH.

12+ Months

Must-

  • 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 Google Cloud Platform including Kubernetes, Docker, and CI/CD

 

We are seeking a Senior Data Scientist with deep expertise in LLMs, NLP, and agent architectures to lead the development of interoperable, self-improving AI agents in the healthcare domain. This role focuses on designing advanced multi-agent systems that interact intelligently across clinical, administrative, and benefits platforms using Agent-to-Agent (A2A) protocols and Model Context Protocols (MCP).

 

Key Responsibilities

  • Design and implement A2A protocols for autonomous task delegation and collaboration among specialized AI agents (e.g., ClaimsAgent, ProviderMatchAgent).
  • Develop MCP pipelines to enable persistent memory and context continuity across multi-turn healthcare interactions.
  • Architect and deploy LLM-orchestrated agent systems for use cases like prior authorizations, benefit optimization, and clinical summarization.
  • Fine-tune domain-specific LLMs and NLP models (e.g., medical BERT, BioGPT) for intent classification and personalized recommendations.
  • Build retrieval-augmented generation (RAG) systems with structured/unstructured healthcare data (e.g., FHIR, ICD-10, EHR).
  • Collaborate on building scalable, secure, and compliant ML pipelines (HIPAA, CMS, NCQA).
  • Lead research in memory-based agents, RLHF, and context-aware planning.
  • Contribute to end-to-end MLOps pipelines for deployment, monitoring, and iteration.

 

Required Qualifications

  • Master s/Ph.D. in CS, ML, NLP, or related field.
  • 7+ years in applied AI, particularly with LLMs, transformers, or agent systems in healthcare.
  • Proficiency with tools like LangGraph, AutoGen, CrewAI, and hands-on A2A protocol development.
  • Proven experience with Model Context Protocols, LLM pipelines, and healthcare NLP.
  • Strong Python skills with libraries such as Hugging Face, LangChain, spaCy, and PyTorch.
  • Understanding of healthcare systems (e.g., claims, eligibility, plan design).
  • Experience with healthcare data standards: FHIR, ICD/CPT, HL7, X12 EDI.
  • Cloud-native development: AWS/Google Cloud Platform/Azure, Docker/Kubernetes, CI/CD.

 

Preferred Qualifications

  • Expertise in MCP + VectorDB for agent memory and dynamic context retrieval.
  • Experience building production-grade LLM agents in healthcare.
  • Background in voice AI, AI navigation, or triage systems.
  • Published work or patents in LLM-based agent systems or contextual AI.

 

Thanks

Yashasvi Hasija

Technical Recruiter | Empower Professionals

......................................................................................................................................

| Phone: x 368 | Fax:

LinkedIn: linkedin.com/in/yashasvi-hasija-6a745625b

100 Franklin Square Drive Suite 104 | Somerset, NJ 08873

 

Certified NJ and NY Minority Business Enterprise (NMSDC)

Employers have access to artificial intelligence language tools (“AI”) that help generate and enhance job descriptions and AI may have been used to create this description. The position description has been reviewed for accuracy and Dice believes it to correctly reflect the job opportunity.