Architect

Design, build, and operate end‑to‑end MLOps pipelines on Github Deploy and manage Databricks workspaces and environments Enable reproducible ML experimentation, training, and deployment Support batch and real‑time inference workloads Implement ML‑specific CI/CD pipelines using Azure DevOps or GitHub Actions Automate Data validation Model training and versioning Model packaging and deployment Integrate pipelines with code, data, and model repositories Manage model versioning, rollbacks, and promotion between environments Implement model monitoring for performance, drift, and data quality Support re‑training and continuous improvement workflows Implement secure ML solutions aligned with enterprise security and compliance standards Enforce identity, access control, and secrets management Ensure traceability and auditability of models and predictions