Role Summary :
You'll architect large-scale autonomous AI ecosystems that power intelligent decision-making across underwriting, claims, and fraud prevention. You'll define the blueprint for explainable, governed, and compliant Agentic AI across the enterprise.
Key Responsibilities :
- Architect enterprise-grade agentic systems for end-to-end claims automation, risk scoring, and policy management.
- Build multi-agent collaboration frameworks for cross-functional reasoning (e.g., Claim Agent + Fraud Agent + Underwriting Agent).
- Implement governance and safety layers enforcing compliance (PHI masking, access controls, explainability).
- Integrate knowledge graphs with RAG pipelines to link medical ontologies (SNOMED, ICD-10, CPT).
- Design self-reflective agents capable of error correction, justification, and escalation to human adjusters.
- Oversee data lineage, observability, and versioned reasoning for auditable AI.
- Partner with actuarial and data science teams to integrate predictive models into reasoning agents.
- Lead teams in developing trustworthy, explainable AI within HIPAA and SOC 2 frameworks.
Technical Skills :
8+ years of experience in AI systems or enterprise architecture, with 3+ in LLM / agentic AI.Expert in LangGraph , Semantic Kernel , Autogen , or custom orchestration frameworks.Deep understanding of health insurance workflows : claims lifecycle, enrollment, policy underwriting, utilization review.Strong foundation in knowledge graphs , vector search , and ontology-based retrieval .Experience integrating FHIR servers , EHR APIs , and data pipelines .Hands-on with cloud-native architectures (Azure Health Data Services, AWS HealthLake).Proven record of designing AI governance frameworks , audit trails , and ethical controls .Preferred : contributions to open-source AI, whitepapers, or regulatory AI committees.