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Industrial MLOps Engineer

Ethan Park

Full-time · Job · Chicago

About the role

Docker, Kubernetes, and MLflow are the core of what we build here to keep predictive maintenance models running on factory floors without a dedicated ops team on site. If that stack excites you, read on. You'll build the deployment pipeline that pushes model updates to edge devices across dozens of factories, with rollback safety built in from the start. It's a role where a bad deployment means a real production line stalling, so reliability isn't optional.

Responsibilities

  • Build deployment pipelines for edge-deployed predictive models
  • Implement safe rollback procedures for factory-floor updates
  • Maintain MLflow-based model versioning and tracking
  • Work with plant engineers to troubleshoot deployment issues

Requirements

  • 6+ years in MLOps or infrastructure engineering
  • Strong Docker and Kubernetes experience
  • Familiarity with MLflow or comparable model tracking tools
  • Comfortable supporting industrial, mission-critical systems

Benefits

  • Fully remote position
  • Competitive salary + equity
  • Health and dental insurance
  • On-call compensation

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Categories

MLOps & AI Infrastructure

Skills

python docker kubernetes mlflow

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Job Type

Full-time

Level

Senior

Language

English

Salary Range

$160k – $200k / year

AI Expertise

MLOps & AI Infrastructure

Skills

python docker kubernetes mlflow

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