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AI Model Serving Infrastructure Engineer

Rolvex Solutions

Remote · Under $60k / year · Full-time · Posted 5 months ago

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About the role

We run 340 ML models in production for 28 clients, and every one of those models needs to stay online, fast, and accurate. When a model goes down at 2 AM, our monitoring catches it. When latency spikes during peak traffic, our autoscaler handles it. When a model starts drifting, our alerts fire. That infrastructure doesn't build itself — and we need another engineer to help maintain and extend it. As a junior infrastructure engineer, you'll handle the day-to-day operations of our model serving platform: deploying new model versions, investigating performance incidents, tuning autoscaling configurations, and building monitoring dashboards. The senior engineers handle architecture changes; you'll handle the operational work that keeps everything running smoothly. Expect a steep learning curve in Kubernetes, container orchestration, and production ML operations. We have a structured onboarding program and pair programming sessions, and you'll be on-call (during business hours only) within your second month. This is the fastest way to learn MLOps — by operating real infrastructure under real constraints.

Fully remote.

Responsibilities

  • Deploy model versions and manage production serving configurations
  • Investigate and resolve performance incidents and latency issues
  • Build and maintain monitoring dashboards for model health metrics
  • Tune autoscaling policies based on traffic patterns
  • Support client onboarding by provisioning serving infrastructure
  • Participate in business-hours on-call rotation for model serving

Requirements

  • 1+ year of software development or DevOps experience
  • Basic understanding of Docker and container concepts
  • Familiarity with Python for scripting and automation
  • Interest in Kubernetes and cloud infrastructure
  • Systematic debugging skills and comfort reading logs

Benefits

  • Fully remote across EU time zones
  • 28 days PTO
  • €1,500 learning budget for certifications
  • Structured onboarding and mentorship
  • Business-hours only on-call — no midnight pages

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Categories

MLOps & AI Infrastructure AI & Machine Learning Engineers

Skills

python docker kubernetes aws fastapi

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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 fastapi mlflow

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