. Own the ML platform/pipelines for training, deployment, monitoring, and retraining across multiple models and teams.
- Build CI/CD for ML, model registry/versioning, and standardized environments (containers, IaC, automated testing).
- Implement observability and controls: drift detection, performance monitoring, alerting, and auditability.
- Partner tightly with Data Science + Data Engineering to standardize handoffs and ensure scalable, governed delivery • Senior-level troubleshooting mindset: reliability, cost/performance tuning, and rapid incident resolution.