AI Transformation Owner, Product & Design

GitLab is the intelligent orchestration platform for DevSecOps. GitLab enables organizations to increase developer productivity, improve operational efficiency, reduce security and compliance risk, and accelerate digital transformation. More than 50 million registered users and more than 50% of the Fortune 100* trust GitLab to ship better, more secure software faster.

The same principles built into our products are reflected in how our team works: we embrace AI as a core productivity multiplier, with all team members expected to incorporate AI into their daily workflows to drive efficiency, innovation, and impact. GitLab is where careers accelerate, innovation flourishes, and every voice is valued. Our high-performance culture is driven by our values and continuous knowledge exchange, enabling our team members to reach their full potential while collaborating with industry leaders to solve complex problems. Co-create the future with us as we build technology that transforms how the world develops software.

Fortune 500® is a registered trademark of Fortune Media IP Limited, used under license. Claim based on GitLab data. Fortune 100 refers to the top 20% ranked companies in the 2025 Fortune 500 list, published in June 2025. Fortune and Fortune Media IP Limited are not affiliated with, and do not endorse products or services of GitLab.

An overview of this role

As an AI Transformation Owner at GitLab, you'll shape your function's AI strategy and build the solutions that deliver it. You are the person responsible for identifying where AI can fundamentally change how your org operates, partnering with your Executive Sponsor to align on the biggest challenges, and driving measurable outcomes against them.

Think of this as a product management role where the product is your org's way of working. You'll manage the full lifecycle: understanding how work flows today, deciding where AI should reshape it, prioritizing what gets built and in what order, and ensuring what ships actually gets adopted. You'll also prototype solutions, configure agents, and prove what's possible before pulling in engineering support to scale it.

You will work closely with an AI Engineer who sits within the Enterprise AI team. Together you form a partnership: you bring the business context, process intelligence, and strategic prioritization. They bring the technical depth, production-grade delivery, and architecture decisions. You'll build working solutions at the no-code and low-code layer, and partner with the AI Engineer on the right approach, tooling, and structure.

What you'll do

Strategy & Prioritization

Own your function's AI strategy, aligned with your Executive Sponsor and business priorities. Understand which metrics matter to the org, identify what will move the needle, define how you'll measure impact, and track progress over time.
Map how work flows across your function end-to-end, including the handoffs upstream and downstream to other orgs. Identify where the real constraints are, not just the ones your team can see. Focus on the 100x problems: where could leveraging AI in a workflow let your org execute it orders of magnitude faster, or at 100x more volume than before?
Manage intake of AI requests, ideas, and pain points from across the function, including via your Champion network. Ensure every team member has a clear route to surface what they need, rather than building independently.
Prioritize strategically against business outcomes and executive guidance. Hold the line on priorities - we cannot change direction every two weeks - and ensure the AI Engineer's time is spent on the highest-impact work.

Adoption & Change Management

Reimagine, not just automate. Challenge your org to think beyond injecting AI into existing workflows. Work with Enterprise AI to spot opportunities to fundamentally rethink how work gets done.
Drive adoption and change management together with the AI Engineer. The best AI solution is worthless if nobody uses it. Create the channels, rituals, and feedback loops that make AI visible in your function: shared spaces for teams to show what they've built, regular office hours, onboarding for new hires, and celebration of wins. Own the rollout and iteration needed to make AI initiatives stick.
Coordinate with Enterprise AI to ensure your function benefits from patterns, tools, and learnings emerging across other parts of the business.
Build and bridge the Champion network in your function. Champions are the peer community that extends your reach beyond what you and the AI Engineer can deliver directly. From early in the role, identify and recruit Champions across sub-teams (5-10% time, formally agreed with their manager), run a regular Champion sync, host demos to the wider function, and act as their bridge to Enterprise AI. Champions are not your reports: you coordinate them, you don't manage them. Without this network, your reach is capped.

Hands-On Building & Agent Operations

Build AI agents using no-code and low-code platforms (e.g. Glean, Workato, similar tools). Go from idea to working prototype without waiting for engineering.
Author and iterate on skills files that define how AI agents behave. Refine instructions based on real usage and share reusable skills across the function.
Configure MCP servers and tools, giving agents access to the business systems they need. Partner with the AI Engineer on what to connect and how to do it securely.
Own your function's fleet of agents. Some agents will be used directly by people in your org. Those that aren't, you own. Either way, you're accountable for their performance: tracking KPIs, running evaluations after model or data changes, and iterating based on what you learn.
Expect to rebuild. AI tools and models evolve fast. The agent you built last month may need to be replaced, not patched. You should be comfortable sunsetting your own work when a better approach emerges, and helping your org stay current rather than attac