- Own the full lifecycle of predictive models in production — architecture, training pipelines, inference infrastructure, deployment, and ongoing model health.
- Build and operate the systems that route model outputs into live product surfaces: search ranking, recommendations, feed ordering, and related user-facing experiences.
- Establish and maintain model monitoring, alerting, drift detection, and retraining cadences to ensure accuracy over time.
- Partner with Data Science, Data Engineering, Product Management, and backend engineering to move validated approaches to production systems.
- Own the decision-making process for leveraging GOAT Group ML infrastructure versus building in-house solutions.
- Contribute to ML infrastructure decisions regarding serving architecture, feature computation, and pipeline orchestration.
- Set technical standards and evaluate how ML systems are built and operated across the pod.
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