Senior Backend Engineer, AI Engineering: Chat at GitLab

Role overview

GitLab builds an intelligent orchestration platform for DevSecOps. This senior backend role works on AI-powered engineering systems inside GitLab’s distributed, remote organisation.

The first role focuses on Duo Chat’s Chat Engine and agentic workflows. The second focuses on finding software-development friction and building reliable AI automation across GitLab’s codebase.

Responsibilities

• Design and ship production backend services and AI-powered workflows using Python and related platform services.
• Build agentic flows, tool integrations, safety checks and evaluation systems that measure correctness, constraints, reliability and cost.
• Integrate APIs, permissions, data models and asynchronous workloads while preserving scalable, maintainable and backward-compatible contracts.
• Diagnose performance, reliability and concurrency problems in distributed systems and improve observability and test coverage.
• Collaborate asynchronously with product, UX, frontend and AI specialists, document patterns, and share reusable tooling.
• Participate in production support or on-call work and contribute to root-cause analysis and resilience improvements.

Qualifications

• Significant experience building and maintaining production Python backends, APIs, data models and long-running workloads.
• Hands-on experience with LLM-backed or agentic systems, tool or function calling, multi-step orchestration and production safety patterns.
• Strong distributed-systems judgment, including coordination, consistency, idempotency, rate limiting and degradation under load.
• Proficiency with Python and willingness to work across Ruby on Rails, Go or other languages used by the relevant GitLab team.
• Experience designing REST or GraphQL APIs, using SQL and relational databases such as PostgreSQL, and diagnosing reliability issues at scale.
• Ability to work autonomously in unfamiliar codebases and communicate effectively in an async-first, globally distributed team.

Technology in this role

• Python: Design and ship production backend services and AI-powered workflows using Python and related platform .
• SQL: Experience designing REST or GraphQL APIs, using SQL and relational databases such as PostgreSQL, and diagnosing reliability issues at .
• Go: Proficiency with Python and willingness to work across Ruby on Rails, Go or other languages used by the relevant GitLab .
• Ruby on Rails: Proficiency with Python and willingness to work across Ruby on Rails, Go or other languages used by the relevant GitLab .
• Ruby: Proficiency with Python and willingness to work across Ruby on Rails, Go or other languages used by the relevant GitLab .
• PostgreSQL: Experience designing REST or GraphQL APIs, using SQL and relational databases such as PostgreSQL, and diagnosing reliability issues at .
• LLM: Hands-on experience with LLM-backed or agentic systems, tool or function calling, multi-step orchestration and production safety .
• large language models: You'll integrate large language models and orchestrate multi-agent flows so customers can work faster and more securely across the software development .
• LangChain: Hands-on experience with agent frameworks such as LangGraph or LangChain is a strong .
• CAN: You'll integrate large language models and orchestrate multi-agent flows so customers can work faster and more securely across the software development .
• GraphQL: Experience designing REST or GraphQL APIs, using SQL and relational databases such as PostgreSQL, and diagnosing reliability issues at .
• Rails: Proficiency with Python and willingness to work across Ruby on Rails, Go or other languages used by the relevant GitLab .

Role-specific candidate guide

Preparation notes based on the employer’s stated responsibilities and qualifications.

Understanding the scope. This Senior Backend Engineer, AI Engineering: Chat position at GitLab is best assessed through the work described in the employer’s posting, rather than the title alone. The available role overview says: “GitLab builds an intelligent orchestration platform for DevSecOps. This senior backend role works on AI-powered engineering systems inside GitLab’s distributed, remote organisation.” Read that statement together with the responsibilities below to understand the likely balance between delivery, coordination and technical decision-making. The role belongs to the AI and data area, but that label is only a guide to the work; the employer’s own requirements remain decisive. Before applying, check the original listing for reporting lines, product or team assignment, employment terms and any details that may have changed since this copy was checked. Where this page says a fact is not specified, treat it as an open question rather than an implied promise.

What the responsibilities signal. The posting specifically names “Design and ship production backend services and AI-powered workflows using Python and related platform services.”; “Build agentic flows, tool integrations, safety checks and evaluation systems that measure correctness, constraints, reliability and cost.”; “Integrate APIs, permissions, data models and asynchronous workloads while preserving scalable, maintainable and backward-compatible contracts.”. For each duty, identify a project where you performed comparable work and be ready to describe the context, your own contribution, constraints, decisions, and measurable result. Explain how you moved from the initial need to implementation, review, release or operational follow-through, where those stages apply. If the work involved other teams or users, distinguish your decisions.

Benefits

• Remote work under the country and hiring restrictions shown in the official listing.
• Benefits supporting health, finances and wellbeing, plus flexible paid time off.
• Parental leave, team member resource groups, equity compensation and an employee stock purchase plan.
• A growth and development fund and opportunities to learn with distributed engineering teams.

About GitLab

GitLab is a remote-first DevSecOps platform company. Its AI Engineering teams build generative and agentic capabilities embedded across the software-development lifecycle.

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