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AI Deployment Reliability Engineer

Aurora Vestergaard

Full-time · Job · Chicago

About the role

We deploy ML models for 20 clients, and each deployment is a production system that real users depend on. When a model serving endpoint goes down at 2 AM, someone needs to fix it — and ideally, the monitoring should have caught it before it went down at all. You'll own deployment reliability: building the monitoring stack that tracks model serving health, designing automated rollback procedures for failed deployments, creating chaos engineering tests that verify resilience, and maintaining the runbooks that on-call engineers use when things break. ML deployments fail in unique ways. A model might serve predictions fine but return garbage because a feature pipeline broke upstream. Latency might spike because input data changed shape. Memory might leak because batch sizes aren't bounded. You'll learn to anticipate these failure modes and build defenses against them.

Responsibilities

  • Build monitoring systems tracking model serving health across 20 client deployments
  • Design automated rollback procedures for failed model deployments
  • Create chaos engineering tests verifying ML deployment resilience
  • Maintain on-call runbooks for common ML serving failure modes
  • Investigate and resolve production incidents with root cause analysis

Requirements

  • 3+ years in SRE, DevOps, or ML infrastructure
  • Kubernetes and Docker expertise for container orchestration
  • AWS experience for cloud infrastructure management
  • Understanding of ML serving patterns and common failure modes
  • Strong debugging and incident response skills

Benefits

  • Fully remote — US time zones
  • Health insurance with dental
  • $2,000 learning and certification budget
  • Growing team with clear career progression

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Categories

MLOps & AI Infrastructure

Skills

python kubernetes docker aws airflow

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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 aws airflow mlflow

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