• Lead agentic AI development, including multi-agent orchestration patterns, agent-to-agent protocols, and reliable tool use at production scale • Own prompt engineering and evaluation workflows including structured outputs, hallucination reduction, and behavioral consistency • Build and own MCP server infrastructure that exposes backup data to AI agents via the Model Context Protocol, enforcing tenant-aware RBAC, query constraints, and safe tool boundaries • Define AI quality benchmarks for retrieval relevance, summarization accuracy, and agent reliability, and drive systematic improvements through eval-driven iteration • Champion security and safety in AI systems, including adversarial prompt hardening, jailbreak resistance, data boundary enforcement, and OWASP LLM Top 10 awareness • Tune AI workflows for performance, cost, latency, and observability across billions of documents in global regions • Mentor engineers on the team, raise the technical bar, and contribute to architecture reviews and design decisions

• Own the AI roadmap. • Set direction across our AI surfaces: voice and computer-use agents that work payers and provider portals, email triage, and the models that extract and structure data from clinical and legal documents. • Push AI deeper. • Take our systems from assisting people, to making decisions, to running whole service requests, and into licensing, enrollment, monitoring, and the customer-facing product. • Make it production-grade. • Build the evals, observability, and human-in-the-loop systems that let us ship models and agents we'll stand behind and back with guarantees. • Lead the data platform. • Own the pipelines, datasets, and tooling behind the models. Our real-world data is our biggest advantage, so make it fast to use and trustworthy to build on. • Build the org. • Hire and grow managers and senior engineers, set the structure, and hold the bar as we scale. Stay close enough to the work to earn their trust in a design review.

• Design & Develop Agentic Systems: Build intelligent agents capable of autonomous planning, reasoning, and task execution, often using LLMs (e.g., GPT-class, LLaMA), multi-modal models, and autonomous workflows • Orchestration & Frameworks: Implement agent orchestration using frameworks like LangChain, AutoGen, CrewAI, Semantic Kernel, or custom solutions • Retrieval-Augmented Generation (RAG): Design and optimize RAG pipelines for enhanced reasoning with external knowledge, including document ingestion, chunking, embeddings, vector stores, and retrieval ranking • Tool & Memory Integration: Develop agents that call APIs, databases, and other tools, maintain memory, and adapt based on outcomes • Evaluation & Monitoring: Create evaluation frameworks for accuracy, grounding, latency, and cost; build observability for agent behavior and failure modes • Model Adaptation: Fine-tune or adapt foundation models (e.g., via LoRA, adapters) for domain-specific use cases • Production Deployment: Deploy GenAI/agentic systems in cloud-native environments with CI/CD, versioning, and runtime safeguards • Cross-Functional Collaboration: Work with data scientists, ML engineers, product teams, and governance/compliance stakeholders

• Lead the architecture, design, and engineering delivery of the organization's agentic AI platform and related solutions • Drive the transformation of existing products, processes, and workflows into scalable agentic AI-enabled capabilities • Oversee end-to-end AI engineering operations, including development, deployment, monitoring, reliability, and continuous improvement • Partner with cross-functional stakeholders to define technical roadmaps, platform standards, and solution priorities aligned to business goals • Establish engineering best practices for AI system performance, scalability, security, governance, and maintainability