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AI Model Registry Engineer

Connor Leclerc

Full-time · Job · London

About the role

Fourteen ML teams deploy models independently, and nobody knows what's in production. Model versions are tracked in spreadsheets. Rollback procedures don't exist. Two teams accidentally deployed conflicting model versions to the same endpoint last month. The model registry needs to be rebuilt from scratch. You'll design and build the central model registry: version control for model artifacts, metadata tracking for lineage and dependencies, approval workflows for production promotion, and the API layer that deployment pipelines use to pull verified model packages. The technical challenge is making the registry useful without making it bureaucratic. ML teams move fast and hate process overhead. Every friction point you add to the deployment workflow is a reason for teams to bypass the registry entirely. You'll need to make the right path the easiest path.

Responsibilities

  • Design the model registry architecture for 14 ML teams
  • Build version control and metadata tracking for model artifacts
  • Implement approval workflows for production model promotion
  • Create APIs for automated model package retrieval by deployment pipelines
  • Minimize workflow friction to ensure team adoption

Requirements

  • 3+ years in MLOps or platform engineering
  • Deep MLflow or model registry tool experience
  • Kubernetes and Docker expertise for artifact management
  • Strong Python and API design skills
  • Understanding of ML deployment workflows and team dynamics

Benefits

  • Fully remote — UK and EU time zones
  • 28 days PTO
  • Equity participation
  • Greenfield platform build with organizational impact

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Categories

MLOps & AI Infrastructure

Skills

python mlflow docker kubernetes aws

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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 mlflow docker kubernetes aws fastapi sql

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