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.

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

• 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.

You will partner closely with Enterprise AI within the Enterprise Technology & AI organization, while remaining embedded in your own function. Enterprise AI provides the technical delivery capability, platforms, and patterns. You provide the business context, prioritization, building at the no-code layer, and adoption muscle. Together, you form the core of your function's AI transformation.

The base salary range for this role's listed level is currently for residents of the United States only. This range is intended to reflect the role's base salary rate in locations throughout the US. Grade level and salary ranges a

Strategy & Prioritization

Own your function's AI strategy,

Map how work flows

Manage intake

Prioritize strategically

Adoption & Change Management

Reimagine, not just automate.

Drive adoption and change management

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.

Hands-On Building & Agent Operations

Build AI agents

Author and iterate on skills files

Configure MCP servers and tools,

Own your function's fleet of agents.

Expect to rebuild.

• 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.
• 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?
• 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.
• 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.
• 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.
• 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.
• 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.
• using no-code and low-code platforms (e.g.
• Glean, Workato, similar tools).
• Go from idea to working prototype without waiting for engineering.
• that define how AI agents behave.
• Refine instructions based on real usage and share reusable skills across the function.
• giving agents access to the business systems they need.
• Partner with the AI Engineer on what to connect and how to do it securely.
• 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.
• 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 attached to what exists today

Business & Strategic

Deep knowledge of your function's operations.

Strategic prioritization skills.

A product management mindset.

Strong communication and influence.

Cross-functional instincts.

You default to understanding how your org's processes affect and are affected by the teams around you - not just optimising in isolation.

Hands-On & Technical

Comfortable building with AI tools.

Ready to learn fast.

Strong conceptual understanding of AI capabilities

Ability to map data flows

• You understand how work moves through your org, where it gets stuck, and why.
• You can trace a process end-to-end and explain how it connects to the teams around it.
• You've managed competing demands before and can make hard calls about what matters most.
• You think in terms of business outcomes, not activity.
• You naturally think about intake, backlog, iteration, and adoption.
• You're comfortable defining success metrics and holding yourself accountable to them.
• You'll be the person saying "not yet" to some teams and "think bigger" to others.
• You need the credibility and interpersonal skills to make both of those conversations land.
• Experience building peer networks or communities of practice. You've recruited and sustained volunteer contributors before, whether as a guild lead, champion program owner, or community organizer. You know how to motivate people whose time you don't directly own.
• You don't need to write production code, but you should be able to build a working agent, configure a skill, connect an MCP server, and troubleshoot when something isn't working.
• Think: power user, not software engineer.
• Experience with or willingness to quickly pick up no-code/low-code AI platforms, prompt engineering, and agent configuration.
• You'll be trained on GitLab's specific tooling, but you should arrive ready to get your hands dirty.
• summarization, classification, generation, automation, agentic workflows - and a commitment to staying current. The landscape shifts constantly. The tools you use today may be obsolete in weeks. You stay on top of new developments so that your org's AI strategy reflects what's actually possible, not what was possible six months ago.
• structured and unstructured. Understand where agents need context, and figure out where humans should interface with automated workflows and at what steps.