• Design, architect, and deploy AI-powered systems that directly impact member experience and operational efficiency • Build and optimize LLM-driven applications using advanced prompt engineering, RAG architectures, and agentic workflows • Design scalable AI pipelines using modern orchestration frameworks (LangChain, LangSmith, LlamaIndex, etc.) • Architect multi-agent systems capable of reasoning, planning, and executing complex multi-step tasks • Implement evaluation and monitoring frameworks to measure AI performance, detect hallucinations, and ensure reliability at scale • Own cloud-native AI infrastructure for performance, scalability, cost efficiency, and security • Partner with Product and Engineering leadership to identify and execute high-impact AI initiatives • Stay at the frontier of the AI ecosystem and translate emerging capabilities into production-ready systems • Establish internal standards, tooling, and best practices for AI development across the organization

• Own AI intake and delivery across Sales and CSG: run intake, clarify business problems, define requirements and success metrics, prioritize the backlog, and drive initiatives from 0 to 1 through release and iteration. • Build and ship AI workflows end-to-end: map current-state processes, identify friction, design future-state workflows, and implement agents and automations that teams can rely on day to day. • Partner cross-functionally to unblock delivery: work day-to-day with Sales, Customer Success, Marketing Ops, Business Technology, Data Science, Finance, and Legal to align on requirements, data usage, governance, and measurable outcomes. • Build with guardrails: embed permissioning, auditability, and compliance requirements into workflows from day one. • Launch what you build: drive rollout with lightweight enablement (training assets, office hours, comms), gather feedback, and continuously improve workflows after release. • Own quality and operational rigor: create test plans, run QA, manage prompt and workflow versioning, and maintain documentation, runbooks, and release notes so delivery is safe and repeatable. • Measure impact and iterate: track adoption and performance, validate results in partnership with Ops and Analytics, and continuously improve workflows based on usage and outcomes.

• Ideate, develop and productionalize AI solutions for healthcare payer use cases • Design and implement state-of-the-art AI models and algorithms using Python and GCP Vertex AI • Collaborate with product teams to identify opportunities for applying AI techniques to solve business challenges and automate processes • Conduct research and stay up to date with the latest advancements in AI

• Designing and delivering enterprise-grade AI/ML and Data Science platforms within complex cloud ecosystems • Building serverless and containerised execution environments for ML and data workloads • Developing secure, scalable patterns for cloud Infrastructure-as-Code and deployments • Defining API standards, observability strategies and architectural guardrails • Establishing and evolving CI/CD pipelines with automation at the core • Leading and mentoring cross-functional engineering and data teams • Translating business requirements into pragmatic architectural solutions • Maintaining technical alignment with product, business and customer goals • Selecting technology approaches that meet non-functional requirements while optimising cost • Continuously researching emerging technologies and best practices and applying them where beneficial