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Junior MLOps Developer

Aurora Vestergaard

Full-time · Job · London

About the role

Our ML platform runs 20 models for internal teams, and the person who built it is now our VP of Engineering. She needs someone to maintain what she built and gradually take over day-to-day operations: monitoring pipeline health, debugging failed training jobs, updating Docker images, and making small improvements. This is a junior role with clear mentorship. You'll start by learning the existing platform — how Airflow orchestrates training jobs, how MLflow tracks experiments, how models get deployed on Kubernetes. Then you'll take over maintenance, and eventually you'll be proposing and building improvements. We're a 25-person company and you'll be the only MLOps person. That sounds scary, but you'll have a senior VP who built the whole thing as your direct mentor. If you want to learn production MLOps by owning a real system from day one, this is the fastest path.

Responsibilities

  • Monitor and maintain ML platform health and pipeline execution
  • Debug failed training jobs and resolve infrastructure issues
  • Update Docker images and manage Kubernetes deployments
  • Track and improve platform reliability metrics
  • Gradually propose and implement platform improvements

Requirements

  • Python proficiency and comfort with command-line tools
  • Basic Docker and container concepts
  • Exposure to Kubernetes or orchestration tools
  • Interest in MLOps and infrastructure engineering
  • Self-motivated learner comfortable with ownership

Benefits

  • Fully remote — UK and EU time zones
  • Direct mentorship from VP of Engineering
  • 25 days PTO
  • Clear career growth path into senior MLOps

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Categories

MLOps & AI Infrastructure

Skills

python docker kubernetes airflow mlflow

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Job Type

Full-time

Level

Junior

Language

English

Salary Range

Under $60k / year

AI Expertise

MLOps & AI Infrastructure

Skills

python docker kubernetes airflow mlflow aws

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