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Senior Data Scientist – AI Agents & LLM Architectures (Fully onsite) (2 open roles)
Length: 3 months (potential extension)
Location: Woodland Hills, CA 91364

*** Will have a AI based tech screen via Glider tool to Validate all skills ***
Overview:

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

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 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 to optimize 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 advanced document understanding, intent classification, and personalized plan recommendations.

• Develop retrieval-augmented generation (RAG) systems and structured context libraries to dynamically ground knowledge from structured (FHIR/ICD-10) and unstructured sources (EHR notes, chat logs).

• Collaborate with engineering and data architecture teams to build secure, explainable, and compliant agentic pipelines aligned with HIPAA, CMS, and NCQA regulations.

• Lead research and prototyping in memory-based agent systems, RLHF (Reinforcement Learning with Human Feedback), and context-aware task planning.

• Contribute to production deployment using MLOps best practices, including 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 applied AI/ML experience with a focus on LLMs, transformers, agent frameworks, or NLP in healthcare.

• Proven hands-on experience with Agent-to-Agent protocols, LangGraph, AutoGen, CrewAI, or other multi-agent orchestration tools.

• Practical knowledge and implementation of Model Context Protocols (MCP) for long-lived conversational memory and modular agent interactions.

• Strong coding expertise in Python with ML/NLP libraries such as Hugging Face Transformers, PyTorch, LangChain, and spaCy.

• Experience with healthcare data standards (FHIR, HL7, ICD/CPT, X12 EDI formats).

• Cloud-native development experience on AWS, Azure, or GCP, including Kubernetes, Docker, and CI/CD pipelines.

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