• Own delivery and business outcomes for AI-powered product initiatives, with accountability for ARR impact, adoption, and capital efficiency. • Lead and grow an engineering team with a focus on output, leverage, and sustainable pace, not headcount growth for its own sake. • Partner closely with Product, Finance, and GTM to prioritize work based on revenue potential, customer value, and cost-to-serve. • Drive features that unlock new revenue, expansion, and retention, while improving unit economics. • Guide architectural decisions with a strong bias toward cost-aware scalability, especially for AI workloads (inference, training, data pipelines). • Establish engineering practices that maximize engineering ROI, shorter cycle times, fewer handoffs, and predictable delivery. • Make disciplined tradeoffs to hit market windows while avoiding long-term cost or maintenance drag. • Track and improve key metrics such as cost per feature, infrastructure spend vs. usage, and revenue per engineering dollar. • Stay technically engaged through design reviews and selective hands-on work, especially where cost, risk, or scale are involved. • Build a culture of ownership, accountability, and financial awareness across the team. • Takes on additional responsibilities and projects as needed to support the success of the team and organization.