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AI Release Gate Automation Engineer

Nathan Yamamoto

Full-time · Job · Chicago

About the role

Deploying AI models is not like deploying software. A model that passes all unit tests can still regress on edge-case inputs, behave differently across model versions with identical weights, or produce outputs that are technically correct but commercially unacceptable. Release gates for AI systems need to encode business logic, not just technical correctness, and they need to run automatically without slowing down the 40 deployments per day our platform handles. You'll design the release gate framework: a configurable system that runs a suite of pre-deployment checks — performance regression tests, fairness metric validation, output distribution comparison, latency profiling, and business rule verification — and makes an automated pass/fail decision with documented reasoning. The framework needs to be extensible so different model types and business contexts can configure their own gate logic without touching the core system. This is a staff-level IC role. You'll set the architecture, build the core framework, and define the patterns that 12 model teams use to configure their own gates. Good judgment on what should be automatic versus what should require human sign-off is as important as technical execution.

Responsibilities

  • Design a configurable release gate framework for AI model deployments
  • Implement performance regression, fairness, and distribution comparison checks
  • Build business rule verification modules that encode domain-specific acceptance criteria
  • Create extensible gate configuration DSL usable by individual model teams
  • Integrate gate framework into CI/CD pipelines with documented pass/fail reasoning
  • Define standards for gate coverage across different model types and risk levels

Requirements

  • 7+ years in MLOps, platform engineering, or software reliability engineering
  • Experience designing deployment automation systems with configurable validation logic
  • Deep Python and Kubernetes skills
  • Strong understanding of statistical test design for model evaluation
  • Track record building systems used across multiple product teams

Benefits

  • Fully remote with flexible schedule
  • Top-of-market compensation
  • Equity package
  • Comprehensive health benefits
  • Annual $5,000 professional development budget

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Categories

MLOps & AI Infrastructure Automation & AI Workflows

Skills

python kubernetes docker aws fastapi

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Job Type

Full-time

Level

Senior

Language

English

Salary Range

$160k – $200k / year

AI Expertise

MLOps & AI Infrastructure Automation & AI Workflows

Skills

python kubernetes docker aws fastapi statistics

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