高级后端工程师，AI工程：聊天

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The same principles built into our products are reflected in how our team works: we embrace AI as a core productivity multiplier, with all team members expected to incorporate AI into their daily workflows to drive efficiency, innovation, and impact. GitLab is where careers accelerate, innovation flourishes, and every voice is valued. Our high-performance culture is driven by our values and continuous knowledge exchange, enabling our team members to reach their full potential while collaborating with industry leaders to solve complex problems. Co-create the future with us as we build technology that transforms how the world develops software.

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An overview of this role

As a Senior Backend Engineer on the Duo Chat team, focused on Chat Engine, you'll build the core AI capabilities behind GitLab Duo Chat, the natural-language and agentic interface to the GitLab DevSecOps platform. Much of your work will involve building flow components and agentic flows in the Flow Registry, the Python framework built on LangGraph within our Duo Workflow Service that powers agentic chat, and integrating them with the GitLab Rails monolith.

You'll own complex backend features from start to finish across both services. You'll integrate large language models and orchestrate multi-agent flows so customers can work faster and more securely across the software development lifecycle. This work sits where GitLab's core platform meets its AI strategy, and reliability, performance, and answer quality directly shape the customer experience.

What you’ll do

• Design and build flow components and agentic flows in the Flow Registry using Python and LangGraph within the Duo Workflow Service. These reusable building blocks power agentic Duo Chat and, increasingly, other AI features across GitLab.

• Develop, ship, and maintain backend features for GitLab Duo Chat across the Python Duo Workflow Service and the GitLab Rails monolith in a secure, well-tested, and performant way.

• Integrate new generative AI models, providers, tools, and multi-agent orchestration patterns into Duo Chat to expand its capabilities and improve answer quality.

• Design, implement, and review GraphQL and Representational State Transfer (REST) application programming interfaces (APIs) and related monolith logic, including chat entry points, permissions, and foundational-flow registration. Keep contracts with frontend clients and host systems reliable and clear.

• Improve debugging, observability, and test coverage using pytest, RSpec, and related frameworks; track and improve latency, error rates, and test coverage so AI-powered chat workflows stay reliable at scale.

• Collaborate with Product, User Experience (UX), frontend, and AI specialists to refine requirements and deliver high-quality improvements through iteration.

• Document standards, patterns, and learnings with other engineers, raising the bar for safe AI integration and evidence-driven engineering.

• Participate in Tier 2 on-call rotations to troubleshoot production issues, contribute to root cause analysis, and strengthen resiliency.

What you’ll bring

• Significant experience building and maintaining production Python backends, including APIs, data models, and asynchronous or long-running workloads.

• Practical experience designing and shipping AI-powered, agentic backend features, including large language model integration, tool or function calling, and multi-agent orchestration. You use sound judgment about large language model limitations and safe use in production.

• Working proficiency in Ruby on Rails, or a strong willingness to learn it. Duo Chat integrates deeply with the GitLab monolith for chat entry points, GraphQL, permissions, and flow registration, and most of GitLab's codebase is written in Ruby.

• Proficiency designing or extending REST or GraphQL APIs with attention to scalability, maintainability, and backward compatibility.

• Strong Structured Query Language (SQL) skills and familiarity with relational databases such as PostgreSQL, including efficient queries and data modeling.

• Ability to find, diagnose, and prevent performance and reliability problems at scale.

• Experience solving technical problems of high scope and complexity and advocating for quality, security, and performance improvements across your team.

• Openness to learning and collaborating in an async-first, distributed team, applying transferable skills from related technologies or domains. Hands-on experience with agent frameworks such as LangGraph or LangChain is a strong plus.

About the team

We're part of GitLab's AI Engineering organization and own the AI-powered chat experience embedded across the GitLab platform. Chat Engine is the backend-focused team within the Duo Chat group.

We're backend, frontend, and AI specialists working asynchronously across time zones, using issues, merge requests, and documentation as our main collaboration tools. Our focus is to expand generative and agentic AI capabilities, improve the performance and reliability of chat workflows, and strengthen the debugging and testing foundations that let us run AI features safely at scale.

For more on how we work, see the team handbook page.

Remote-Global
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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