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ML Platform Engineer

Inovex Intelligence

Remote · $100k – $130k / year · Full-time · Posted 5 months ago

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About the role

Our ML platform serves 8 internal teams and supports over 40 models in production. It's built on GCP with Kubernetes, MLflow, and Airflow — and it works. But as the company grows, the platform needs to evolve. We need someone to help make it self-service, scalable, and delightful for the ML engineers who use it. You'll focus on the developer experience layer: building internal CLIs, improving the model deployment workflow, adding automated testing for model artifacts, and reducing the time from experiment to production. Right now it takes 2 weeks to deploy a new model — we want that down to 2 days. This is a platform engineering role with an ML twist. You'll collaborate with ML engineers as your internal customers, understanding their pain points and building tools that make their lives easier.

Fully remote.

Responsibilities

  • Build and maintain internal ML platform tools and CLIs
  • Reduce model deployment time from 2 weeks to 2 days
  • Automate model artifact testing and validation pipelines
  • Design self-service workflows for model registration and deployment
  • Collaborate with ML teams to understand and address platform pain points

Requirements

  • 3+ years in platform or infrastructure engineering
  • Strong Kubernetes and Docker experience
  • Familiarity with MLflow and Airflow
  • Python proficiency and experience building developer tools
  • GCP experience (GKE, Cloud Build, Artifact Registry)

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Categories

MLOps & AI Infrastructure

Skills

python kubernetes docker mlflow airflow

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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 mlflow airflow gcp fastapi

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