3. MLOps Manager — Role Mandate Mandate: Design and operationalize the enterprise Azure + Databricks MLOps operating model, with centrally governed production deployment and controlled market access to non-production environments.
• Define the end-to-end deployment operating model across sandbox, development, test/UAT, and production.
• Create production access policies, separation-of-duties rules, approval gates, and exception processes.
• Establish the central MLOps team as the owner of production model deployment and release management.
• Design reusable deployment patterns for batch, real-time, and GenAI workloads on Azure + Databricks.
• Define MLflow and Unity Catalog standards for model registration, versioning, promotion, and lineage.




• Implement enterprise monitoring standards for model performance, drift, reliability, alerts, and rollback.
• Own FinOps governance, including tagging, usage attribution, Databricks compute policies, budgets, alerts, and showback/chargeback.
• Define security patterns using Entra ID, RBAC, managed identities, Key Vault, private networking, and audit logging.
• Create measurable SLAs for model onboarding, deployment, incident response, and production support.
• Lead stakeholder alignment across market DS teams, central DS, engineering, security, platform, and finance.

📌 MLops Manager (Bengaluru)
🏢 Nityo Infotech
📍 Bengaluru