ML Ops Professional

• Build and maintain CI/CD & CT (Continuous Training) pipelines for ML models using Azure DevOps and Databricks Asset Bundles.
• Automate data preprocessing, training, inference and retraining workflows for large-scale ML deployments.
• Implement incremental backfills and rolling window retraining for time-series forecasting.

"," Deployment & Infrastructure

• Design job clusters and compute policies in Databricks for optimal cost-performance trade-offs.
• Implement multi-environment deployment flows (Dev QA (stage) Prod) with approvals and rollback strategies.
• Deploy ML models to production with monitoring hooks for performance and drift detection.

"," Data & Model Governance

• Integrate with Unity Catalog for secure, compliant data and model storage.
• Set up model versioning, lineage tracking and reproducibility using MLflow.
• Establish dataset and feature versioning using tools like Databricks Feature Store.

"," Monitoring & Observability

• Implement structured logging for model metrics, system performance and data quality checks.
• Integrate monitoring tools (e.g., Azure Application Insights) for alerting and dashboards.
• Develop automated retraining triggers based on performance degradation.

"],qualifications:["B.Tech/BCA or any Graduation","Experience with time-series forecasting at scale (e.g., Prophet, Sarima, XGBoost).","Experience in retail demand forecasting and/or energy sector analytics.","Knowledge of feature engineering at scale with distributed systems."],description:""