• Take a comprehensive view of the ASIC development lifecycle, identifying cross-stage bottlenecks and opportunities where automation and AI can improve predictability, convergence, and turnaround time. • Identify, curate, and leverage real-time data to enable effective AI models and analytics, working with infrastructure teams to ensure scalable and secure data pipelines. • Establish quantitative metrics to measure efficiency, quality, and cycle-time improvements; use data-driven insights to guide methodology decisions and prioritize investments. • Serve as a technical catalyst within both the team and the wider company by sharing best practices, publishing internal guidelines, and mentoring engineers on emerging AI-enabled development techniques. • Track advances in AI, EDA, and hardware design research, evaluating their applicability and guiding strategic bets that align with long-term product and technology roadmaps.