• Own the end-to-end AI product strategy, grounded in technical feasibility and real-world constraints. • Translate model capabilities, data limitations, and evaluation results into clear product decisions. • Make hard trade-offs across quality, latency, cost, reliability, and user experience. • Work daily with ML, backend, and mobile engineers on design, evaluation, and iteration. • Define success metrics and feedback loops across offline evaluation, online experiments, and human feedback. • Drive execution with clear specifications, risk awareness, and disciplined prioritization. • Ensure AI features ship quickly, safely, and reliably into production. • Own AI product quality across UX, correctness, and outcomes.