Own end-to-end model lifecycle in production: architecture, training, deployment, health.
Route model outputs into live surfaces: search, ranking, recommendations.
Model monitoring, drift detection, and retraining cadences.
Collaborate with DS, DE, PM, and backend teams to ship features.
Decide when to leverage ML infra or build in-house solutions.

🎯 Requirements

7+ years in engineering with production ML systems.
End-to-end ownership: training pipelines through deployed inference.
Advanced ML/AI knowledge and statistical models for ecommerce.
Strong Python; SQL; DBT; Airflow; solid software engineering fundamentals.
Experience with ranking, retrieval, or recommender systems.
ML lifecycle tooling: experiment tracking, model versioning, drift detection.

🎁 Benefits

401K, PTO, health, dental, vision, disability, and life insurance.
Remote-friendly with flexible work arrangements.
Growth opportunities within a leading fashion tech group.

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