You excel in these key competencies: • Strategic Vision & Enterprise Thinking: Ability to translate business strategy into a clear, forward-looking engineering vision-anticipating market shifts in AI/automation and aligning long-term technical roadmaps to measurable business outcomes • Executive Influence & Stakeholder Leadership: Exceptional ability to influence and partner across C-suite, Board, and cross-functional leaders (Product, GTM, Finance), while representing engineering with credibility, clarity, and confidence • Organizational Leadership & Talent Development: Proven capability to build, scale, and inspire high-performing global teams-developing senior leaders, fostering accountability, and creating a culture of innovation, inclusivity, and engineering excellence • Decision-Making & Operational Judgment: Strong, data-driven decision-making in complex, high-stakes environments-balancing speed, risk, cost, and quality, especially across AI investments, architecture choices, and build-vs-buy tradeoffs • Customer-Centric Mindset & Business Acumen: Deep understanding of customer needs and enterprise requirements, with the ability to convert insights into impactful engineering priorities that drive adoption, value realization, and revenue growth. You will be a great fit if you have: • 15+ years in software engineering; 7+ years leading large-scale, multi-team organizations at VP/SVP level in enterprise SaaS • Demonstrated success shipping AI-native products in production with strong governance, telemetry, and quality gates-not just integrating AI, but building around it • Deep expertise scaling cloud systems (multi-tenant, high-throughput) on AWS/Azure/GCP; strong instincts for bottlenecks and performance engineering • Proven ownership of reliability and security at scale: SRE practices, SLOs/SLIs/Error Budgets, secure SDLC, vulnerability management, incident response, and postmortems • Track record of portfolio execution with short release cycles, outcome-based planning, and products with demonstrated customer adoption • Mastery of modern engineering: Kubernetes, Docker, microservices, Java and/or polyglot stacks, event streaming, and cloud-native data systems • Hands-on fluency with the AI/ML stack: Python, PyTorch/TensorFlow, vector DBs, feature stores, LangChain/Semantic Kernel, MLflow/KServe, and model evaluation and observability • Strong executive presence: able to influence at CEO/Board level and inspire large, distributed teams with clarity and conviction.