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AI Deployment Engineer

Nathan Yamamoto

Contract · Job · Los_Angeles

About the role

We have 12 ML models that work beautifully in notebooks. Getting them into production is another story. Our data science team is brilliant at modeling but doesn't have deployment experience, and our two backend engineers are buried in feature work. We need a deployment specialist to bridge the gap. This is a 6-month contract-to-hire role. Your job: take our trained models and build repeatable deployment pipelines — containerization, API serving, monitoring, and rollback procedures. You'll establish the patterns and tooling that our team will use going forward. We're not looking for someone to just push containers. We need someone who can assess our current setup, recommend a deployment strategy, implement it, and teach the team how to maintain it. If you've been the person who turns 'it works on my machine' into 'it works in production,' this is your role.

Responsibilities

  • Build containerized deployment pipelines for 12 ML models
  • Design API serving infrastructure with FastAPI and Docker
  • Implement model monitoring, logging, and rollback procedures
  • Establish CI/CD patterns for model deployment
  • Train the data science team on deployment best practices

Requirements

  • 3+ years in DevOps or ML deployment roles
  • Strong Docker and Kubernetes experience
  • Familiarity with AWS (ECS, ECR, Lambda, API Gateway)
  • Experience with MLflow or similar model management tools
  • Ability to document and teach deployment processes clearly

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Categories

MLOps & AI Infrastructure Automation & AI Workflows

Skills

python docker kubernetes aws fastapi

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Job Type

Contract

Level

Mid-Level

Language

English

Salary Range

$80k – $100k / year

AI Expertise

MLOps & AI Infrastructure Automation & AI Workflows

Skills

python docker kubernetes aws fastapi mlflow

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