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AI Blue-Green Testing Engineer

Nathan Yamamoto

Full-time · Job · Los_Angeles

About the role

Model updates should be seamless — users shouldn't notice the transition from v2.3 to v2.4. Blue-green deployments make this possible by running both versions simultaneously, routing traffic to the new version only after automated testing confirms it performs at least as well as the old one. You'll build the blue-green testing framework for our ML platform: automated comparison testing between model versions, traffic routing rules that gradually shift load from green to blue, rollback automation that detects degradation and reverts within seconds, and the reporting dashboard that shows deployment teams exactly how the new version compares. The testing challenge for ML is harder than for traditional software. 'Works correctly' isn't a binary check — it's a statistical comparison across multiple metrics, stratified by user segments, with enough traffic to reach significance. You'll design testing protocols that provide confident deployment decisions within the smallest possible traffic exposure.

Responsibilities

  • Build automated comparison testing between model versions in blue-green deployments
  • Design gradual traffic routing rules shifting load after testing confirmation
  • Implement rollback automation detecting degradation and reverting within seconds
  • Create deployment comparison dashboards for version performance analysis
  • Design testing protocols providing statistical confidence with minimal traffic exposure

Requirements

  • 3+ years in MLOps or deployment engineering
  • Kubernetes and Docker expertise for parallel deployment management
  • Understanding of statistical testing for model version comparison
  • MLflow or model registry experience
  • Python proficiency for testing automation and tooling

Benefits

  • Fully remote — US West Coast hours
  • Competitive salary with equity
  • $2,000 learning budget
  • Building reliable ML deployment infrastructure

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Categories

MLOps & AI Infrastructure

Skills

python kubernetes docker mlflow aws

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Job Type

Full-time

Level

Mid-Level

Language

English

Salary Range

$80k – $100k / year

AI Expertise

MLOps & AI Infrastructure

Skills

python kubernetes docker mlflow aws sql

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