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AI Container Orchestration Engineer

Nathan Yamamoto

Full-time · Job · Los_Angeles

About the role

We deploy ML models for 12 clients, and each deployment is its own snowflake — different frameworks, different serving requirements, different scaling patterns. The current process involves too much manual container configuration and too many deployment scripts held together with duct tape and good intentions. You'll standardize our model deployment infrastructure: building reusable container templates, designing the CI/CD pipeline for model artifacts, implementing blue-green deployments for model updates, and creating the monitoring stack that catches serving issues before clients notice. The goal is simple: any model, any framework, deployed to production in under 2 hours with zero manual container configuration. Right now it takes 2 days. You'll close that gap.

Responsibilities

  • Build reusable container templates for diverse ML framework deployments
  • Design CI/CD pipelines for automated model artifact packaging and deployment
  • Implement blue-green deployment strategies for zero-downtime model updates
  • Create monitoring and alerting for model serving health and performance
  • Standardize deployment processes to reduce time-to-production from 2 days to 2 hours

Requirements

  • 3+ years in DevOps or container orchestration engineering
  • Deep Kubernetes and Docker expertise
  • Experience with ML model serving and inference optimization
  • AWS skills for cloud infrastructure management
  • Python proficiency for tooling and automation

Benefits

  • Fully remote — US time zones
  • Competitive salary with equity
  • $2,500 learning and conference budget
  • Direct impact on deployment reliability for 12 clients

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Categories

MLOps & AI Infrastructure

Skills

python kubernetes docker aws mlflow

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Job Type

Full-time

Level

Mid-Level

Language

English

Salary Range

$100k – $130k / year

AI Expertise

MLOps & AI Infrastructure

Skills

python kubernetes docker aws mlflow fastapi

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