Backend Engineer (Python),AI Engineering: Agent Foundations

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Remote from: UK, Canada, Poland
Salary: Undisclosed
Department: Software Engineering
Employment: Full Time
Experience: Open level
Published: 16 Oct 2026
Apply before: 15 Nov 2026
Listing views: 53
Application actions: 2

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AI Summary

The role, at a glance.

GitLab is hiring an intermediate Python backend engineer to build core services for its agentic AI platform, including the Duo Workflow Service and AI Gateway. The role focuses on secure, scalable backend development, agentic workflows using LangChain and LangGraph, and well-designed REST and GraphQL APIs. The engineer will improve automated testing and AI-flow evaluations, investigate performance issues, and participate in Tier 2 on-call support. Success requires strong asynchronous written communication and close collaboration with product, UX, frontend, backend, and AI engineering partners in an async-first remote environment. The role is available remotely in Canada, Poland, and the United Kingdom.

Role DNA

A quick view of the complexity, pace, ownership and collaboration implied by the job description.

Job Complexity

4/5

EasyHard

Pace & Pressure

4/5

RelaxedFast-paced

Autonomy Level

4/5

GuidedFull ownership

Communication Load

5/5

IndependentCollaborative

AI insightThis is a technically demanding backend role combining production Python engineering with emerging LLM-agent frameworks, API design, testing, performance work, and production on-call responsibilities. The engineer must make sound technical decisions in a distributed, cross-functional environment while building reliable AI platform foundations.

Salary analysis

Estimated compensation compared with the broader US market for similar roles.

Estimated job median (per year)Market rate

$135,000

US market range (per year)$110k–$165k

0$181.50k

AI insightThe yearly pay period is inferred from the job context; it was not explicitly stated by the employer. No employer salary was disclosed. The annual compensation estimate is an AI-generated USD market estimate for an intermediate Python backend engineer building production AI-platform services; yearly pay cadence is inferred from the full-time professional role and prevailing market convention.

Core skills

Skills and capabilities most closely associated with this opportunity.

Python Backend Development LangGraph LangChain LLM Applications REST APIs GraphQL API Design Asynchronous Processing Data Modeling pytest Automated Testing Performance Optimization Observability Root Cause Analysis

Sample interview questions

How would you design a reliable agentic workflow for a production software-development platform?

I would begin by defining explicit inputs, outputs, state transitions, permissions, and failure modes. I would make tool calls observable and idempotent where possible, add timeouts and retry policies, and use evaluation datasets to test both expected behavior and unsafe or degraded paths before release.

Describe how you would maintain backward compatibility while evolving a REST or GraphQL API.

I would first identify existing consumers and document the current contract. I would favor additive changes, version or deprecate breaking REST changes with a migration period, and use GraphQL deprecations rather than removing fields abruptly. Contract tests, telemetry, and clear migration documentation would help validate adoption before removal.

What testing strategy would you use for LangGraph-based flows?

I would combine unit tests for individual nodes and tools, integration tests for state transitions and external-service boundaries, and evaluation tests using representative prompts and expected outcomes. I would also test failure handling, authorization boundaries, deterministic fixtures where possible, and regression cases found through production incidents.

How would you investigate a performance bottleneck in a Python backend service?

I would start with metrics and traces to determine whether latency comes from application code, database access, network dependencies, queueing, or resource saturation. I would reproduce the issue with representative load, profile the relevant path, implement a targeted improvement, and confirm the result through benchmarks and production monitoring.

How do you work effectively in an asynchronous, cross-functional remote team?

I communicate decisions and context in concise written proposals, document assumptions and trade-offs, and make work visible through small, reviewable iterations. I seek early feedback from product, design, and engineering partners, then use documented outcomes and follow-ups to keep execution aligned across time zones.

Opportunity details

About this role.

GitLab is the intelligent orchestration platform for DevSecOps. GitLab enables organizations to increase developer productivity, improve operational efficiency, reduce security and compliance risk, and accelerate digital transformation. More than 50 million registered users and more than 50% of the Fortune 100* trust GitLab to ship better, more secure software faster.

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.

*Fortune 500® is a registered trademark of Fortune Media IP Limited, used under license. Claim based on GitLab data. Fortune 100 refers to the top 20% ranked companies in the 2025 Fortune 500 list, published in June 2025. Fortune and Fortune Media IP Limited are not affiliated with, and do not endorse products or services of GitLab.

An overview of this role

As an Intermediate Backend Engineer (Python) in GitLab’s Agent Foundations stage, you’ll go beyond using AI tools. You’ll help build the foundations for GitLab’s agentic AI experiences: the platform that lets AI agents work across the whole software delivery lifecycle, not just write code. That covers the reusable building blocks agents and flows are made from as well as our core harness, Duo Developer.

Most of your work will be backend engineering in Python, using libraries such as LangGraph to build secure, reliable and scalable features in our AI Gateway. You’ll also work across the stack where needed. You’ll work closely with engineers across the stage and other AI Engineering teams. This is a high-visibility area where GitLab’s core platform meets its AI strategy.

What you’ll do

Develop, ship and maintain features for the Duo Agent Platform in the Duo Workflow Service, the AI Gateway and, where needed, the GitLab monolith. Keep them secure, well-tested and performant.
Build and improve agentic flows and their reusable parts with LangChain and LangGraph, including the Flow Registry.
Work with Product Management, UX, frontend, backend and AI specialists to refine requirements and ship improvements in small iterations.
Design, implement and review APIs (including GraphQL and REST) and the backend logic behind them, with reliable, scalable and clear contracts for clients such as the GitLab monolith, IDE extensions and Duo CLI.
Improve and extend automated testing (pytest and other frameworks), including evaluations of agent and flow behavior, to strengthen quality and give developers faster feedback.
Share standards, patterns and learnings with other engineers to raise the bar for responsible AI integration and evidence-driven engineering.
Join Tier 2 on-call rotations to troubleshoot production issues, contribute to root cause analysis, and improve observability and resiliency.
Keep an eye on the competitive landscape to help keep GitLab’s agent platform best-in-class as more work becomes agentic.

What you’ll bring

Experience building and maintaining production Python backend applications, including APIs, asynchronous or background processing, and data models.
Experience with LLM application frameworks such as LangGraph, or designing and shipping AI-powered backend features or harnesses.
Skill in designing or extending APIs (REST, GraphQL or gRPC) with attention to scalability, maintainability and backward compatibility.
Comfort working across the stack in a mature codebase. Familiarity with Ruby on Rails and/or JavaScript and Vue is a plus.
Experience finding and fixing performance bottlenecks in applications.
Clear written and verbal communication, including technical proposals and documentation, in a remote, async, cross-functional team.
Openness to learning and to applying skills from related technologies or fields.

About the team

The Agent Foundations stage builds the foundations for GitLab’s agentic AI experiences. We provide the platform, catalog and execution features that help teams deliver resilient agents and flows to customers. The stage has three groups: Agent Developer, AI Catalog and Agent Execution. Between them, they own the GitLab Duo Developer foundational flow and the Flow Registry; the AI Catalog, including custom agents, custom flows and external agent integrations; and agent execution, agent tools and agent observability.

Our engineers work async-first across EMEA, AMER, and India timezones. We work with teams across AI engineering and the rest of GitLab so the Duo Agent Platform can power many of GitLab’s AI features.

How GitLab Supports Full-Time Employees

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.

Privacy Policy: Please review our Recruitment Privacy Policy. Your privacy is important to us.

GitLab is proud to be an equal opportunity workplace and is an affirmative action employer. GitLab’s policies and practices relating to recruitment, employment, career development and advancement, promotion, and retirement are based solely on merit, regardless of race, color, religion, ancestry, sex (including pregnancy, lactation, sexual orientation, gender identity, or gender expression), national origin, age, citizenship, marital status, mental or physical disability, genetic information (including family medical history), discharge status from the military, protected veteran status (which includes disabled veterans, recently separated veterans, active duty wartime or campaign badge veterans, and Armed Forces service medal veterans), or any other basis protected by law. GitLab will not tolerate discrimination or harassment based on any of these characteristics. See also GitLab’s EEO Policy and EEO is the Law. If you have a disability or special need that requires accommodation, please let us know during the recruiting process.