GitLab - Senior Backend Engineer (AI), Pipeline Execution
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Requirements
• 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. • 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 • 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. • Country Hiring Guidelines:
Responsibilities
• 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. • Examples of Agentic CI work we have planned for the upcoming year: • AI Pipeline Builder, the foundational CI agent that auto-creates pipelines for new projects and serves as the launchpad for onboarding new CI users. • Automate the Fix a Failing Pipeline flow at scale – from dogfooding on internal GitLab projects through to safe, controlled rollout for customers, solving real infrastructure and scalability challenges. • Build the instrumentation and observability layer that makes agentic CI trustworthy — trigger volume dashboards, retry rates, cost safeguards — so we can measure what’s working, catch what isn’t, and iterate with confidence. • Harden the CI pipeline execution infrastructure that these agents depend on: database access patterns, background processing, and job orchestration built to handle the additional load that AI-driven automation introduces at enterprise scale. • 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.
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