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Senior Backend Engineer (Ruby), AI Engineering: Duo Agent Platform Tools

Vaga remota de AI Engineering com fit claro de localização do candidato.

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Develop, ship, and maintain features and improvements for Agentic Tools across the GitLab monolith backend in a secure, well-tested, and performant way.
Collaborate with Product Management, UX, frontend, backend, and AI specialists to refine requirements and deliver high-quality, iterative product enhancements.
Design, implement, and review GraphQL, REST APIs and related backend logic, ensuring reliability, scalability, and clear contracts with frontend clients.
Improve and extend automated testing, including RSpec and other frameworks, to strengthen quality, reliability, and developer feedback loops.
Share standards, patterns, and learnings with other engineers, raising the bar for responsible AI integration and evidence-driven engineering across the team.
Participate in Tier 2 on-call rotations to troubleshoot production issues, contribute to root cause analysis, and enhance observability and resiliency
Bring a sharp eye to the competitive landscape, helping us understand what it takes to keep GitLab’s MCP server best-in-class in an increasingly agentic world.

Experience building and maintaining production applications with Ruby on Rails, including work with background jobs, APIs, and data models.
Comfortable working across the stack in a mature codebase. Familiarity with Python backends and/or Vue frontends a plus.
Proficiency in designing or extending REST and/or GraphQL APIs with attention to scalability, maintainability, and backward compatibility.
Practical experience designing and shipping AI-powered backend features and integrations, including sound judgment about large language model limitations and responsible use in production.
Strong SQL skills and familiarity with relational databases such as PostgreSQL, including writing efficient queries and understanding data modeling.
Ability to identify and address performance bottlenecks at the application and, where relevant, infrastructure layers.
Openness to learning, collaborating in an async-first, distributed team, and applying transferable skills from related technologies or domains.