• Lead the AI quality roadmap, progressively integrating AI across all QA functions. • Evaluate and implement AI tools for test generation, self-healing suites, and intelligent defect triage. • Define AI-specific KPIs to track the shift from manual to AI-driven quality practices. • Lead the transition of QA engineers from manual testers to quality architects. • Upskill the QA org through targeted training in prompt engineering, AI orchestration, and meta-QA practices. • Drive innovation by fostering an experimental culture focused on piloting AI capabilities, measuring results, and rapid iteration. • Own and deliver measurable improvements in customer-reported defects and critical severity rates. • Implement AI prioritization to focus testing on high-impact code changes and defect-prone areas. • Embed quality practices early in the SDLC, shifting the focus from "test-and-find" to "prevent-and-detect."