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AI DevOps Engineer

Aiden Ferrari

Full-time · Job · New_York

About the role

Our ML engineers ship great models. Getting those models into production used to take 3 weeks and a prayer. We've cut it to 5 days, but we want same-day. That's your mission. You'll own the CI/CD pipeline for model deployment: automated testing of model artifacts, container builds, canary deployments on GKE, and rollback procedures. You'll also maintain the observability stack — dashboards, alerts, and logging that tell us when a model starts drifting before customers notice. The team is 3 ML engineers and 2 DevOps engineers. You'll be the bridge between both worlds, understanding enough ML to ask the right questions and enough infrastructure to build the right answers.

Responsibilities

  • Build CI/CD pipelines for automated model testing and deployment
  • Design canary deployment strategies for model updates on GKE
  • Own the observability stack for model serving — dashboards, alerts, logging
  • Implement automated rollback procedures for model quality regressions
  • Partner with ML engineers to reduce deployment friction

Requirements

  • 3+ years in DevOps or SRE with CI/CD pipeline experience
  • Strong Kubernetes experience (GKE preferred)
  • Familiarity with ML workflows and model serving
  • Experience with GCP services (Cloud Build, Artifact Registry, Monitoring)
  • Python proficiency for tooling and automation

Benefits

  • Fully remote — US East Coast hours
  • Competitive salary with annual bonus
  • Full benefits including dental and vision
  • $2,500 professional development budget

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Categories

MLOps & AI Infrastructure

Skills

python kubernetes docker gcp airflow

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Job Type

Full-time

Level

Mid-Level

Language

English

Salary Range

$100k – $130k / year

AI Expertise

MLOps & AI Infrastructure

Skills

python kubernetes docker gcp airflow mlflow fastapi

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