Job Title: Data Scientist
Location: Woodland Hills CA
Duration: Contract
Term: 6 months
Job Description:
Experience Desired: 12 Years.
- 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.
Key Skills:
Data Scientist Agentic AI LLM Kubernetes AWS/Azure.
Job Title: Data Scientist Location: Woodland Hills CA Duration: Contract Term: 6 months Job Description: Experience Desired: 12 Years. Design and implement Agent-to-Agent (A2A) protocols enabling autonomous collaboration negotiation and task delegation between specialized AI agents (e.g. ClaimsAg...
Job Title: Data Scientist
Location: Woodland Hills CA
Duration: Contract
Term: 6 months
Job Description:
Experience Desired: 12 Years.
- 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.
Key Skills:
Data Scientist Agentic AI LLM Kubernetes AWS/Azure.
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