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AI Blue-Green Model Deployment Engineer

Aurora Vestergaard

Full-time · Job · Berlin

About the role

Deploying a new model version shouldn't feel like defusing a bomb. Blue-green deployment eliminates the risk: the new model runs alongside the old one, receives a small percentage of traffic, and only takes over fully once metrics confirm it's performing at least as well. If anything goes wrong, traffic reverts to the old model in under 30 seconds. You'll build the deployment orchestration that manages this process: spinning up the new model environment, configuring traffic splitting, monitoring comparison metrics in real time, and automating the promotion or rollback decision. The system needs to handle 340 models across 28 clients, each with different SLOs, comparison metrics, and rollback triggers. The deployment platform team is four engineers. As the junior engineer, you'll focus on the traffic management and metric comparison layers. Your senior teammates handle the underlying Kubernetes orchestration; you handle the deployment workflow that decides when it's safe to proceed.

Responsibilities

  • Build deployment workflows managing blue-green model transitions
  • Implement traffic splitting with configurable percentages
  • Create real-time metric comparison between old and new model versions
  • Automate promotion and rollback decisions based on SLO compliance
  • Handle deployment configuration for client-specific requirements
  • Monitor deployment health and alert on anomalies

Requirements

  • 1+ year of software development or DevOps experience
  • Basic understanding of Docker and Kubernetes concepts
  • Familiarity with REST APIs and Python scripting
  • Interest in MLOps and deployment engineering
  • Systematic approach to testing deployment workflows

Benefits

  • Fully remote across EU time zones
  • 28 days PTO
  • €1,500 learning budget
  • Structured onboarding and mentorship
  • Build deployment infrastructure for hundreds of ML models

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Categories

MLOps & AI Infrastructure AI & Machine Learning Engineers

Skills

python docker kubernetes aws mlflow

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Job Type

Full-time

Level

Junior

Language

English

Salary Range

Under $60k / year

AI Expertise

MLOps & AI Infrastructure AI & Machine Learning Engineers

Skills

python docker kubernetes aws mlflow fastapi

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