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Junior AI Deployment Documentation Engineer

Aurora Vestergaard

Full-time · Job · Toronto

About the role

We don't care where you work from. We care about output, clear communication, and ownership of the small things that make a deployment pipeline trustworthy. Right now, every deployment runbook on our MLOps team lives in someone's head or a stale wiki page. You'll build the tooling that auto-generates deployment documentation directly from our Kubernetes and MLflow configs, so runbooks stay accurate without anyone remembering to update them. This is a great first MLOps role if you're comfortable with Python scripting and want to learn production deployment infrastructure from people who will actually review your PRs line by line.

Responsibilities

  • Build scripts that auto-generate deployment documentation from Kubernetes and MLflow configs
  • You'll spend most of your time in our deployment pipeline codebase learning it inside out
  • Keep runbook templates in sync as infrastructure changes are merged
  • Partner with senior engineers to identify undocumented tribal knowledge worth capturing
  • Triage documentation gaps reported by on-call engineers after incidents

Requirements

  • 1-2 years of hands-on Python scripting experience
  • Basic familiarity with Docker and Kubernetes concepts
  • Strong written communication skills
  • Eagerness to learn production MLOps practices from a senior team

Benefits

  • Fully remote, async-first team
  • Mentorship from senior MLOps engineers
  • Health insurance
  • Learning stipend for courses and certifications

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Categories

MLOps & AI Infrastructure Automation & AI Workflows

Skills

python docker kubernetes mlflow

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Job Type

Full-time

Level

Junior

Language

English

Salary Range

$60k – $80k / year

AI Expertise

MLOps & AI Infrastructure Automation & AI Workflows

Skills

python docker kubernetes mlflow

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