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AI Deployment Traffic Shadowing Engineer

Mia Levy

Full-time · Job · Chicago

About the role

Our deployment pipelines are growing faster than our confidence in them. Every new model version ships with some risk, and right now we only find out after real users hit it. You'll build traffic shadowing infrastructure that mirrors a slice of production traffic to candidate models before they go live, comparing outputs and catching regressions before customers ever see them. This becomes a standard step in every future model release across the platform.

Responsibilities

  • Build traffic shadowing infrastructure for candidate model releases
  • Own output comparison tooling between shadow and production models
  • Partner with release engineering to integrate shadowing into CI/CD
  • Reduce regression incidents caught after full rollout

Requirements

  • 4+ years in MLOps or backend infrastructure engineering
  • Strong Kubernetes and Docker experience
  • Familiarity with MLflow or similar model tracking tools
  • Experience with GCP a plus

Benefits

  • Fully remote position
  • Competitive salary + equity
  • Health and dental insurance
  • Flexible working hours

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Categories

MLOps & AI Infrastructure

Skills

python kubernetes docker mlflow 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

python kubernetes docker mlflow gcp

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