• Architect and deploy LLM-powered and agentic AI solutions that transform claims intake, policy interpretation, fraud detection, and resolution workflows. • Design end-to-end retrieval-augmented generation (RAG) systems leveraging enterprise knowledge bases, policy documents, SOPs, and historical claims data. • Build autonomous and semi-autonomous agents capable of reasoning, planning, and executing multi-step claims processes. • Develop stateful workflow orchestration layers that manage context, memory, and task sequencing across interactions. • Implement planning and reflection loops that decompose complex claims scenarios into structured subtasks. • Enable dynamic tool use through function calling and secure API integrations with claims systems, CRM platforms, document repositories, and analytics tools. • Develop document intelligence pipelines using LLMs for summarization, entity extraction, classification, validation, and timeline reconstruction. • Design structured prompt frameworks that enforce deterministic outputs and domain-aware reasoning. • Build multi-agent systems that coordinate document review, coverage analysis, compliance checks, and decision support. • Implement human-in-the-loop checkpoints for escalation, review, and override of AI-driven decisions. • Develop guardrails, output validation layers, and hallucination mitigation strategies. • Enforce structured outputs using schemas, type validation, and deterministic post-processing logic. • Optimize token consumption, inference latency, and cloud infrastructure costs. • Deploy scalable AI microservices using containerization and cloud-native architectures. • Implement monitoring for model drift, retrieval quality degradation, reasoning failures, and workflow breakdowns. • Maintain detailed audit logs of model decisions, agent reasoning steps, and tool executions. • Develop evaluation frameworks to test reasoning accuracy, workflow completion rates, and system reliability. • Collaborate with data engineering to build embedding pipelines, feature stores, and vector indexing strategies. • Ensure compliance with Responsible AI standards, data privacy regulations, and enterprise governance policies. • Partner with claims operations leadership to embed AI capabilities directly into adjuster and supervisor workflows. • Measure business impact through cycle-time reduction, automation coverage, fraud detection lift, and operational efficiency gains.