Leads and scales AI engineering teams responsible for production LLM systems, agents, integrations, and shared AI platforms. Oversees architecture, delivery, reliability, cost, latency, evaluation, observability, governance, security, and operational practices. Partners with product and business leaders to prioritize investments, manage dependencies, and establish reusable engineering standards. Develops engineering leaders and drives alignment across teams while ensuring safe, scalable AI adoption.

Own the vision, roadmap, and delivery of AI use-case products, including LLM systems, agents, and evaluation pipelines. Translate customer and business needs into technical product requirements, make build or buy decisions, define AI quality and business metrics, and guide launches through governance and regulatory review. Lead cross-functional teams, manage executive stakeholders, establish AI product standards, mentor product managers, and ensure product quality, safety, adoption, and impact.

Lead and deliver productionized ML solutions across audience, marketing, pharma direct, and employer analytics. Design segmentation, propensity, attribution, and identity models; apply causal inference and experimentation; operationalize models with MLOps; evaluate generative AI/LLM use cases; set technical roadmap and mentor data scientists to drive measurable business impact.