Senior Backend Engineer, AI Engineering: Chat

GitLab GitLab · Enterprise · United Kingdom · AI Engineering

Senior Backend Engineer on the Duo Chat team, focused on building the core AI capabilities for GitLab Duo Chat, a natural-language and agentic interface to the GitLab DevSecOps platform. The role involves building agentic flows using Python and LangGraph, integrating LLMs, and ensuring reliability, performance, and answer quality.

What you'd actually do

  1. 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.
  2. 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.
  3. Integrate new generative AI models, providers, tools, and multi-agent orchestration patterns into Duo Chat to expand its capabilities and improve answer quality.
  4. 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.
  5. 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.

Skills

Required

  • production Python backends
  • APIs
  • data models
  • asynchronous or long-running workloads
  • AI-powered, agentic backend features
  • large language model integration
  • tool or function calling
  • multi-agent orchestration
  • Ruby on Rails
  • REST or GraphQL APIs
  • scalability
  • maintainability
  • backward compatibility
  • Structured Query Language (SQL)
  • relational databases
  • PostgreSQL
  • efficient queries
  • data modeling
  • performance and reliability problems at scale
  • technical problems of high scope and complexity
  • quality, security, and performance improvements
  • async-first, distributed team
  • transferable skills

Nice to have

  • LangGraph
  • LangChain

What the JD emphasized

  • building flow components and agentic flows
  • agentic chat
  • multi-agent orchestration
  • large language model integration
  • tool or function calling
  • multi-agent orchestration
  • sound judgment about large language model limitations and safe use in production

Other signals

  • building agentic systems
  • integrating LLMs
  • production backend development