- 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