Drimva

GitLab

Senior CX Platform Engineer

$139K–$235K

Remote, Canada +3 · Remote · Senior · Full-time · Posted Oct 7, 2026

Location
Remote, Canada
Remote, United States
Canada
United States of America
Experience
Years not specified; welcomes candidates with varying levels of experience.

About the job

Build and operate production CX applications, AI agents, workflows, and platform infrastructure that support GitLab’s Customer Experience teams.

What you'll do

  • Build agents, multi-step workflows, MCP servers, and enterprise-system connectors, with evaluations, tracing, and safety controls.
  • Develop and maintain the CX application platform and applications, shipping tested, observable, secure Python and TypeScript through CI/CD.
  • Partner with CX teams to prototype solutions, create reusable patterns and documentation, and mentor colleagues in agentic development.

What they ask for

  • Experience building and operating production software, owning work from ambiguity through production, and designing for handoff.
  • Daily hands-on production use of coding agents and experience building with LLMs beyond chat, including tool use, MCP servers, agent loops, and evaluations.
  • Strong Python with FastAPI or Flask and TypeScript with Vue or React; experience with API design, authentication, authorization, and secure data handling.
  • Hands-on AWS ECS, RDS, Lambda, Terraform, and Docker experience; enterprise REST and GraphQL integrations; fluency with Git, merge requests, and CI/CD.

Nice to have

  • Agent harness customization, GitLab Duo Agent Platform custom agents and flows, or AI Catalog publishing.
  • Experience with customer success, professional services, or learning platforms; Snowflake; enterprise agent governance; or technical writing, talks, or open-source contributions.

Pay

$139,200 — $235,200 USD (base salary)

Tools

Claude Code, GitLab Duo, MCP, Python, TypeScript, AWS ECS, RDS, Lambda

AI focus

Build production AI agents and workflows for CX work; use coding agents as primary development tools; develop MCP integrations and agent harnesses with evaluations, tracing, governance, and safety controls.

Model-written summary · the original posting is authoritative

First seen Oct 10, 2026 · Last verified Nov 4, 2026 (UTC)