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