• Lead the architecture and delivery of enterprise-grade LLM and agentic AI systems that transform claims, risk, and operational workflows. • Define technical strategy for retrieval-augmented generation (RAG), multi-agent orchestration, and autonomous workflow automation. • Design and implement advanced agentic systems capable of planning, reasoning, tool selection, execution, reflection, and recovery. • Architect stateful, memory-aware AI systems that manage long-running claims processes across multiple touchpoints. • Build multi-agent collaboration models that coordinate coverage analysis, document validation, fraud signals, compliance checks, and decision support. • Establish orchestration frameworks that manage task routing, context persistence, structured outputs, and failure handling. • Design secure tool integration layers connecting agents to claims systems, policy platforms, data warehouses, document repositories, and external data services. • Implement deterministic guardrails, schema validation, and output verification pipelines to reduce hallucination and execution risk. • Lead development of document intelligence systems leveraging LLMs for summarization, entity extraction, discrepancy detection, and structured data reconstruction. • Define prompt engineering standards and reusable reasoning templates for consistent, domain-aware outputs. • Oversee embedding strategies, vector indexing architecture, retrieval optimization, and knowledge grounding approaches. • Design evaluation frameworks to measure reasoning depth, workflow completion accuracy, hallucination rates, latency, and cost efficiency. • Implement observability layers that track agent decisions, tool usage, retrieval effectiveness, and drift across models and prompts. • Drive optimization strategies for token efficiency, caching, batching, and inference scaling. • Ensure compliance with Responsible AI principles, enterprise governance standards, audit requirements, and regulatory constraints. • Partner with enterprise architecture, cybersecurity, and data governance teams to define secure deployment patterns. • Mentor engineers on LLM orchestration patterns, workflow decomposition, and safe agent design. • Translate executive-level business objectives into scalable AI platform capabilities. • Lead proof-of-concepts through full production deployment with measurable ROI outcomes. • Continuously evaluate emerging foundation models, orchestration frameworks, and agent tooling for enterprise readiness.