• ML platform: Productionize training and inference (batch/real-time), establish CI/CD for models, data/versioning practices, and model governance • Feature & model lifecycle: Centralize feature generation (e.g., feature store patterns), manage model registry/metadata, and streamline deployment workflows • Observability & quality: Implement monitoring for data quality, drift, model performance/latency, and pipeline health with clear alerting and dashboards • Engineering excellence: Refactor research code into reusable components, enforce repo structure, testing, logging, and reproducibility • Cross-functional collaboration: Work with DS/Analytics/Engineers to turn prototypes into production systems, provide mentorship and technical guidance • Roadmap & standards: Drive the technical vision for ML platform capabilities and establish architectural patterns that become team standards