Senior Vice President of Engineering

Position Summary

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.

Your Responsibilities

• 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-hands, 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.