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1 day ago Be among the first 25 applicants Dice is the leading career destination for tech experts at every stage of their careers. Our client, Cardinal Integrated Technologies Inc, is seeking the following. Apply via Dice today! Hi, We have a job opportunity with one of our clients for a Senior Data Scientist (19420-1). If interested, please respond back to me with the below-required details or appreciate it if you would be able to refer an ex-colleague/team member/friend looking for similar roles. CardinalIT offers a referral bonus of $500 for each such referral, to show gratitude and appreciation for this gesture. Updated Resume: Expected W2/C2C: Availability to start: - Looking forward to seeing your reply. Position : Senior Data Scientist (19420-1 & 19419-1) Duration : 6+ Months Contract Location : Mason, OH - 5D Onsite Must Have Skills: Skill 1 7+ Years Exp - AI agent architectures, LLMs, NLP developing A2A Protocols and Model Context Protocols (MCP) Skill 2 - 7+ Years Exp - LLMs and NLP models (e.g., medical BERT, BioGPT) Skill 3 - 7+ Years Exp - retrieval-augmented generation (RAG) Skill 4 7+ Years Exp - coding experience in Python, with proficiency in ML/NLP libraries Skill 5 - 7+ Years Exp - healthcare data standards like FHIR, HL7, ICD/CPT, X12 EDI formats. Skill 6 - 7+ Years Exp - 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. Client Job 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., 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. -- Best Regards, Abhishek Singh | Assistant Manager Recruitment | Cardinal Integrated Technologies Inc Direct: +1 Ph: Ext 337 Email: Linkedin Url: 707 Alexander Rd, Ste 202 Princeton, NJ 08540 USA. "Bringing IT Together!" 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