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AI Feature Store Migration Architect

Aubrey Okafor

Full-time · Job · New_York

About the role

What we already have: a production feature store serving 200+ features across 15 ML models, built on a custom internal system that's become a maintenance burden. What we're building: a migration path to a modern, open-source feature store architecture without disrupting the ML models that depend on those features in real time. As the AI Feature Store Migration Architect, you'll own the technical design and phased migration of Velmox's feature store to a Feast or Tecton-based architecture. That means designing the migration plan, building compatibility shims that let existing models continue to serve during cutover, validating feature value parity across old and new systems, and decommissioning the legacy system without downtime. This is a senior role with end-to-end ownership of a high-stakes infrastructure migration. You'll work with ML engineers who depend on features in production, data engineers who own the pipelines feeding the store, and platform leadership who set the timeline. If you've migrated production ML infrastructure before, you'll know exactly what this project demands.

Responsibilities

  • Own the technical design and phased migration plan for the feature store
  • Build compatibility shims ensuring existing ML models continue serving during cutover
  • Develop and run feature value parity validation across old and new feature store systems
  • Coordinate migration timeline with ML engineering, data engineering, and platform leadership
  • Execute decommissioning of the legacy feature store with zero production downtime

Requirements

  • 5+ years ML infrastructure or data platform engineering
  • Deep experience with feature store systems (Feast, Tecton, Hopsworks, or similar)
  • Strong Python, SQL, and Apache Spark skills
  • Track record managing infrastructure migrations without production incidents
  • Experience with MLflow for experiment tracking and model lineage

Benefits

  • Fully remote
  • Top-of-market compensation with equity
  • Health, dental, and vision
  • $5,000 annual engineering development budget
  • Quarterly architecture review sessions with senior leadership

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Categories

MLOps & AI Infrastructure AI & Machine Learning Engineers

Skills

python sql airflow mlflow spark

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Language

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AI Expertise

MLOps & AI Infrastructure AI & Machine Learning Engineers

Skills

python sql airflow mlflow spark

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