Senior Vice President of Engineering

Requirements You're a deeply technical, hands‑on executive with a proven track record of shipping AI-native products at scale, elevating engineering craftsmanship, and turning ambitious AI bets into measurable business outcomes 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 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 growthWhat the job involves We're seeking an SVP of Engineering to lead our global engineering organization at a defining moment in the convergence of Automation and AI. You will own end‑to‑end engineering strategy and execution, driving the vision for AI‑native offerings at scale, from foundational architecture to AI‑powered product delivery Partnering closely with Product, Design, Data, and GTM leaders, you'll embed intelligence across our full platform, enabling enterprises to build, automate, and operate smarter This is a pivotal moment in Automation and AI, and you'll shape how we lead it This role will report to our Chief AI Development Officer Setting the engineering vision and multi‑year roadmap aligned to company strategy; translating business goals into clear technical outcomes and AI‑driven portfolio priorities Leading and scaling a multidisciplinary org (Apps, Platform, AI/ML) with VP/Director leaders and 100+ engineers across geographies Establishing architecture guardrails and clear standards for quality, security, privacy, and reliability—with AI governance built in from the ground up Partnering with Product & Design on outcome‑based roadmaps and customer value hypotheses; shaping pricing and packaging strategy for AI‑native features with PM & Finance Owning SDLC and delivery excellence—predictable releases, high change success rate, and reduced cycle time through robust CI/CD, automation, and trunk‑based development at scale Driving reliability and performance (SLOs/SLIs/Error Budgets, 99.9x+ availability), capacity planning, and cloud cost efficiency through rigorous FinOps and unit economics discipline Partnering with Product and Data Science to define and ship AI‑native capabilities: intelligent agents, automation co‑pilots, autonomous workflow orchestration, RAG pipelines, and safety‑by‑design governance Building and scaling ML/LLM platform foundations—feature stores, vector databases, prompt and model management, evals, red teaming, and monitoring for drift, toxicity, and PII leakage Establishing model lifecycle and compliance practices (data provenance, auditability, bias testing, human‑in‑the‑loop review) and optimize latency and cost across hosted and self‑managed models Recruiting, developing, and retaining top‑tier engineering leaders; build an inclusive, high‑accountability culture that celebrates craftsmanship, AI innovation, and sustainable pace Implementing succession planning, leadership pipelines, and clear career ladders; raising the bar through calibration, mentoring, and principled performance management Being a visible technical voice—internally (all‑halls, engineering demos) and externally (conferences, customer briefings, analyst interactions) Serving as a trusted executive partner to Product, Sales, Customer Success, and Support; represent engineering at the Board and with strategic customers as needed Converting customer feedback into engineering priorities; ensure compliance, data residency, and enterprise‑grade requirements are built into the roadmap Owning engineering budgets, vendor strategy, and build‑vs‑buy decisions; ensure efficient spend across cloud, data/ML infrastructure, and tooling with measurable ROI Leading risk management (operational, technical, privacy), quarterly business reviews, and executive reporting with clear KPIs Leading technical diligence for acquisitions; own integration plans across systems, data, security, and people—aligning product and architecture roadmaps post‑close #J-18808-Ljbffr