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MLOps Engineer

Mia Levy

Full-time · Job · Los_Angeles

About the role

PyTorch, Airflow, and Kubernetes are the core of what we build. If you've ever been the person who gets paged when a model serving endpoint goes down at 2am, you already know what this role is about — and you know how to build systems so that page never happens again. We run 30+ models in production across GCP, serving real-time predictions for logistics and supply chain optimization. Our MLOps team (currently 3 engineers) is responsible for the entire lifecycle: training infrastructure, model registry, CI/CD for model artifacts, serving, and monitoring. We're looking for someone who can lead our push toward fully automated model retraining and deployment. You'll design the architecture, implement it, and mentor the team on operational best practices. Strong opinions loosely held are welcome.

Responsibilities

  • Architect and maintain model serving infrastructure on GKE
  • Build CI/CD pipelines for automated model retraining and deployment
  • Design monitoring and alerting for model performance drift
  • Own the MLflow model registry and experiment tracking setup
  • Mentor junior MLOps engineers on infrastructure best practices

Requirements

  • 5+ years in MLOps or infrastructure engineering
  • Deep Kubernetes experience (GKE preferred)
  • Production experience with Airflow and MLflow
  • Strong Python skills and familiarity with FastAPI
  • Experience with GCP services (Vertex AI, GCS, BigQuery)

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Categories

MLOps & AI Infrastructure

Skills

python kubernetes docker airflow mlflow

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Job Type

Full-time

Level

Senior

Language

English

Salary Range

$160k – $200k / year

AI Expertise

MLOps & AI Infrastructure

Skills

python kubernetes docker airflow mlflow gcp fastapi

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