versioning of prompts, tools, and policies, cost controls, rate-limiting, and fail-safe mechanisms Bridge data science and platform engineering – translate experimentation needs into GCP infrastructure, scalable compute patterns, and reproducible development environments Productionize data science code – convert research-grade notebooks into tested, modular, production-grade Python services, batch … real-time environments Hands-on experience with analytical data warehouses (BigQuery or equivalent) and workflow orchestration (Airflow or similar) Experience deploying ML systems on GCP, including Cloud Run, GCS, and IAM Infrastructure-as-Code experience (Terraform or equivalent) Experience with feature engineering, model lifecycle management, and ML evaluation Demonstrated ability ...