CyberSearch
Senior Data Scientist – AI Agents & LLM Architectures
CyberSearch, Los Angeles, California, United States, 90079
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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
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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(Fully onsite) (2 open roles) Length: 3 months (potential extension) Location: Woodland Hills, CA 91364
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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