Senior Backend Engineer, AI – Pipeline Execution at GitLab

About the role

Senior Backend Engineer (AI) shaping the core infrastructure at GitLab to enhance CI/CD workflows. Collaborating on AI integration for improved performance and usability in high-demand environments.

Collaborate with Engineering, Product, and UX partners to refine priorities: where we can move faster, where we’re missing data, and where there’s whitespace to innovate.
Contribute to defining what success looks like across our AI agents, ensuring we’re not just shipping, but learning from how features perform in production.
Keep a close eye on the competitive landscape and emerging AI-native DevOps tools, helping us understand what it takes to keep GitLab CI best-in-class in an increasingly agentic world.
Design, build, and operate backend features that make GitLab CI fast, reliable, and easy to use at scale.
Implement AI-powered and agentic CI capabilities that integrate with GitLab’s Duo Agent Platform.
Instrument, monitor, and improve CI systems using data, observability, and safe rollout practices.
Write secure, well-tested Ruby on Rails code in our monolith, improving existing features while reducing technical debt.
Collaborate cross-functionally with Product, UX, and Infrastructure, mentoring others and raising engineering standards across the Verify stage.

Strong Ruby on Rails backend experience in a large, production codebase.
In-depth experience building and operating AI-powered backend features in production.
A data- and observability-driven approach to diagnosing issues, improving reliability, and validating impact.
Clear written and verbal communication, with a collaborative, mentoring mindset in a remote, async environment.
Hands-on experience building, running, and debugging high-traffic production systems, ideally in CI, workflow orchestration, or adjacent infrastructure-heavy domains.
Nice to have: Experience with AI agents or agentic frameworks (for example, LangChain or similar technologies) and building agentic workflows in production environments.
Strong PostgreSQL skills, including data modeling, query tuning, and scaling large tables through proactive performance investigation and remediation.