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MLOps Engineer

Aiden Ferrari

Full-time · Job · Amsterdam

About the role

We just closed our Series A and our serverless ML platform is onboarding new customers faster than our deployment tooling can comfortably handle. We need a hands-on MLOps engineer to close that gap before it becomes a bigger problem. What we have already: a working serverless inference layer on GCP and a handful of internal scripts holding the deployment process together. What's missing: proper MLflow experiment tracking, Airflow-orchestrated retraining, and Kubernetes-based scaling that doesn't require someone babysitting it at 2am. Your role is to build that missing layer and hand it off to the platform team once it's stable.

Responsibilities

  • Stand up MLflow for experiment tracking across all model teams
  • Build Airflow DAGs for scheduled retraining jobs
  • Migrate ad hoc deployment scripts to a proper Kubernetes workflow
  • Reduce on-call incidents tied to manual deployment steps

Requirements

  • 3+ years of MLOps or platform engineering experience
  • Hands-on Kubernetes and Docker experience
  • Familiarity with MLflow and Airflow
  • Comfortable working on GCP

Benefits

  • Fully remote position
  • Stock options
  • Home office budget
  • Flexible working hours

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Categories

MLOps & AI Infrastructure

Skills

docker kubernetes mlflow airflow gcp

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Job Type

Full-time

Level

Mid-Level

Language

English

Salary Range

$100k – $130k / year

AI Expertise

MLOps & AI Infrastructure

Skills

docker kubernetes mlflow airflow gcp

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