An overview of this role

As an Engineering Manager, AI Engineering: Workflow Catalog, you will guide the team building GitLab’s AI catalog, which helps customers and internal teams create, share, version, and govern AI agents and workflows across GitLab. This role sits at the center of GitLab’s work on AI and the Duo Agent Platform, with responsibility for helping a team of engineers deliver systems that let AI agents run at scale with the right controls in place. You will report through engineering leadership while managing a team of 9 engineers, and your work in the first year will focus on giving the team clarity, improving execution, and shipping customer-facing capabilities that make AI agents easier to build, manage, and trust.

This is a hands-on management opportunity for someone who wants to have impact through both people management and technical judgment. You’ll help shape how software teams use AI in GitLab, stay close to technical design and day-to-day development, and partner closely with product and UX to turn complex ideas into practical features.

What you’ll do

• Guide a distributed team of frontend, backend, and AI engineers working on the Workflow Catalog to deliver roadmap commitments on schedule.
• Coach engineers through feedback, enablement, and clear expectations to improve performance, growth, and retention across the team.
• Work with product and UX counterparts to define requirements and deliver AI features that address customer needs and improve adoption.
• Participate in technical design discussions and contribute engineering judgment to build scalable, reliable systems that support AI agents running at scale.
• Remove blockers, create clarity, and help the team maintain steady delivery in a fast-moving environment.
• Coordinate execution across planning, development, and delivery to improve predictability, delivery speed, and quality.
• Promote a team culture grounded in inclusion, curiosity, technical craft, and continuous delivery to strengthen collaboration and engineering excellence.
• Work **with teams across GitLab’s AI space to enable the broader Duo Agent Platform and ship aligned AI capabilities across the product.

What you’ll bring

• Experience managing effective engineering teams and assisting engineers with coaching, feedback, and career development.
• Solid technical background in software development, with the ability to engage deeply in architecture and engineering judgment.
• Hands-on familiarity with agentic AI in software development and interest in how AI is changing the way software is built.
• Ability to work effectively in fast-paced environments with evolving requirements and continuous delivery practices.
• Clear communication skills and the ability to work effectively with engineering, product, UX, and other stakeholders across a distributed organization.
• Focus on building an inclusive team environment where engineers can do effective work together.
• Experience with AI product development, observability, reliability engineering, Ruby, or TypeScript is useful, and we welcome candidates with adjacent or transferable experience.

About the team

The Workflow Catalog. We are distributed across three continents but unified by the desire to shape the future of software engineering. We're hard at work creating a product and the infrastructure that changes software development processes at scale. We're partnering with teams both within the AI space as well as all over GitLab to ensure that the Duo Agent Platform can become the backbone of many AI features at GitLab. Examples of such initiatives include Agent Skills, a reusable format for defining agent instructions that can be shared across agents, flows, CI/CD jobs, and IDE chat, and automatic flow generation that lets users describe what they want in plain language rather than having to learn the underlying YAML syntax.

This is an opportunity to guide a team in one of the most exciting areas of GitLab and contribute to the fundamental changes that are happening to the SDLC right now.

An overview of this role

As a Staff Backend Engineer (Python) on the Duo Chat team in AI Engineering, you'll lead the backend architecture that powers GitLab Duo Chat across the GitLab DevSecOps platform. You'll solve hard problems in building reliable, secure, and scalable AI-powered chat workflows so customers can plan, write, review, and secure code faster, with confidence. This is a hands-on technical leadership role where you'll set direction for how we integrate and evolve large language model providers (including Google Vertex AI) across Ruby on Rails and Python services, raise the bar on observability and testing, and guide the team through ambiguous, high-impact technical decisions. Over your first year, you'll be expected to drive key architectural choices, reduce technical debt that slows iteration, and help the team ship durable improvements to response quality, reliability, and maintainability.

Some examples of our projects:

• Integrate new generative AI models and providers into GitLab Duo Chat to expand capabilities and improve response quality
• Improve debugging, observability, and test coverage for AI-powered chat workflows to increase reliability at scale

What you'll do

• Define the technical architecture and technical roadmap for the Duo Chat group, aligning backend execution with product direction and engineering priorities
• Solve the highest-scope and most ambiguous backend problems, delivering secure, well-tested, performant solutions with minimal guidance
• Integrate and extend generative AI capabilities in GitLab Duo Chat, including large language models (LLMs) and providers such as Google Vertex AI
• Develop, ship, and maintain backend features across Python and Ruby on Rails services that power Duo Chat experiences across the GitLab platform
• Design, implement, and review GraphQL application programming interface (API) contracts and supporting backend logic to ensure reliability, scalability, and clear frontend integrations
• Improve observability, debugging workflows, and incident readiness by strengthening logging, tracing, and production troubleshooting practices
• Drive code quality and long-term maintainability by setting internal standards, leading code reviews, and identifying and reducing technical debt
• Mentor engineers across the team and participate in Tier 2 on-call rotations, contributing to root cause analysis and follow-up improvements to resiliency and testing (including RSpec)

What you'll bring

• Production experience building and operating backend services in Python, including background jobs, APIs, and data models
• Ability to define and evolve technical architecture by weighing trade-offs, selecting patterns and tools, and setting a clear technical direction for others to follow
• Experience setting and driving a technical roadmap in partnership with product and engineering stakeholders
• Proficiency designing and maintaining REST and/or GraphQL APIs with attention to scalability, maintainability, and backward compatibility
• Hands-on experience integrating large language models into applications, including prompt design and building features powered by generative AI
• Strong SQL skills and experience working with relational databases such as PostgreSQL, including efficient queries and data modeling
• Experience mentoring engineers through code review, architectural guidance, and shared standards, and communicating complex technical decisions in a clear, async-first way
• Comfort contributing in a mature codebase across Python and Ruby on Rails, with openness to learning and applying transferable skills from related technologies or domains

About the team

The Duo Chat team sits within GitLab's AI Engineering organization and is responsible for building and evolving GitLab Duo Chat, the AI-powered chat experience embedded across the GitLab DevSecOps platform. You'll work with a small, cross-functional group of backend, frontend, and AI specialists who collaborate asynchronously across time zones, using GitLab issues, merge requests, and documentation as the primary way of working. The team focuses on integrating and scaling generative AI capabilities (including providers like Google Vertex AI), improving reliability and performance, and strengthening debugging, observability, and testing workflows so customers can safely use AI to plan, write, review, and secure their code across GitLab.