Senior Data Scientist // Woodland Hills, Los Angeles, CA -- Locals only
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Shankar 3SBC
Oct 17, 2025, 12:15:22 PM (2 days ago) Oct 17
to Shankar 3SBC
Hi,
Please share resumes to Shankar.a...@3sbc.com
Role: Senior Data Scientist
Location: Woodland Hills, Los Angeles, California ( Need Locals)
12+ Resumes and Local to CA Only
Need 12+ years 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.
• 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.
•
• 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 GCP including Kubernetes, Docker, and CI/CD.
• 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 GCP 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.
• 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.
Additional Skills: MCPc,core nlp,AI,Azure cloud
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