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AI Continuous Integration Architect

Zoe Levy

Full-time · Job · New_York

About the role

Shipping ML models to production should be as boring as deploying a web app. At most companies, it isn't — releases take days, rollbacks are manual, and nobody knows which model version is actually serving traffic. We've fixed this for 90 enterprise clients, and now we need someone to push our CI/CD platform to the next level. You'll own the architecture of our ML continuous integration system — the pipelines that validate model artifacts, run automated quality gates, orchestrate canary deployments, and handle rollbacks when metrics degrade. The current system handles 800 model deployments per week; we're targeting 3,000 by Q4 without adding manual checkpoints. This is a principal-level IC role. You'll set technical direction for a team of six, make architecture decisions that affect platform reliability, and work directly with our largest clients to understand their deployment requirements. No people management unless you want it — we have a separate engineering manager for that.

Responsibilities

  • Design the next-generation ML CI/CD architecture for scale and reliability
  • Build automated quality gates for model validation pre-deployment
  • Implement canary deployment and automated rollback systems
  • Set technical standards for the platform engineering team
  • Work directly with enterprise clients on deployment requirements
  • Evaluate and integrate new orchestration tools as the ecosystem evolves

Requirements

  • 7+ years in platform or infrastructure engineering with ML deployment experience
  • Deep expertise in Kubernetes, Docker, and CI/CD pipeline design
  • Experience building systems that handle thousands of deployments per week
  • Strong opinions on reliability engineering and operational excellence
  • Ability to translate client requirements into platform capabilities

Benefits

  • Fully remote with async-first culture
  • Top-of-market salary plus equity
  • Unlimited PTO with 4-week minimum
  • $5,000 annual conference and learning budget
  • Home office setup allowance

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Categories

MLOps & AI Infrastructure Automation & AI Workflows

Skills

python docker kubernetes airflow mlflow

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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 docker kubernetes airflow mlflow aws

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