AI Transformation Owner, Product & Design

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 attached to what exists today.

What you'll bring

Business & Strategic

Deep knowledge of your function's operations. 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.

Strategic prioritization skills. You've managed competing demands before and can make hard calls about what matters most. You think in terms of business outcomes, not activity.

A product management mindset. You naturally think about intake, backlog, iteration, and adoption. You're comfortable defining success metrics and holding yourself accountable to them.

Strong communication and influence. 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.

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.

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.

Hands-On & Technical

Comfortable building with AI tools. 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.

Ready to learn fast. 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.

Strong conceptual understanding of AI capabilities - 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.

Ability to map data flows - structured and unstructured. Understand where agents need context, and figure out where humans should interface with automated workflows and at what steps.

About the team

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 are determined through interviews and a review of education, experience, knowledge, skills, abilities of the applicant, equity with other team members, alignment with market data, and geographic location. The base salary range does not include any bonuses, equity, or benefits. See more information on our benefits and equity. Sales roles are also eligible for incentive pay targeted at up to 100% of the offered base salary.

United States Salary Range $203,200—$345,600 USD

How GitLab Supports Full-Time Employees

Benefits to support your health, finances, and well-being

Flexible Paid Time Off

Team Member Resource Groups

Equity Compensation & Employee Stock Purchase Plan

Growth and Development Fund

Parental Leave

Please note that we welcome interest from candidates with varying levels of experience; many successful candidates do not meet every single requirement. Additionally, studies have shown that people from underrepresented groups are less likely to apply to a job unless they meet every single qualification. If you're excited about this role, please apply and allow our recruiters to assess your application.

Country Hiring Guidelines: GitLab hires new team members in countries around the world. All of our roles are remote, however some roles may carry specific location-based eligibility requirements. Our Talent Acquisition team can help answer any questions about location after starting the recruiting process.

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