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AI Shadow Deployment Comparison Engineer

Mia Levy

Full-time · Job · Chicago

About the role

The comparison framework you'll build will run silently behind every model deployment our platform customers ship, catching regressions before real users ever see them. This role owns shadow deployment infrastructure — routing a copy of production traffic to candidate models and diffing outputs at scale. Cross-functional squads here run on defined sprints, clear ownership, and weekly syncs with the infrastructure leadership team. You'll work closely with MLOps and platform engineering to make shadow testing the default, not the exception.

Responsibilities

  • Own shadow deployment infrastructure for candidate model testing
  • Build output diffing tools to catch silent regressions
  • Partner with mlops on making shadow testing the default
  • Report on deployment risk trends to leadership

Requirements

  • 5+ years with mlflow and production ml deployment
  • Deep experience with docker and kubernetes
  • Comfortable owning infrastructure that other teams depend on

Benefits

  • Fully remote position
  • Competitive salary plus equity
  • Health, dental, and vision insurance
  • Annual bonus

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Categories

MLOps & AI Infrastructure AI & Machine Learning Engineers

Skills

python mlflow docker kubernetes aws

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Job Type

Full-time

Level

Senior

Language

English

Salary Range

$160k – $200k / year

AI Expertise

MLOps & AI Infrastructure AI & Machine Learning Engineers

Skills

python mlflow docker kubernetes aws

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