- 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.

Required Skills & Experience :

Core MLOps Skills :

- ML pipeline automation (Azure DevOps, GitHub Actions).

- Databricks (Asset Bundles, Unity Catalog, Feature Store).

- Model registry and experiment tracking (MLflow, Weights & Biases or similar).

- Cloud platforms (Azure mandatory).

Programming & Tools :

- Python (pandas, PySpark, scikit-learn, Prophet, ML/DL frameworks).

- Bash/PowerShell scripting.

- Git and branching strategies for ML projects.

Testing & Quality :

- Data validation, schema enforcement and model testing frameworks.

- CI/CD quality gates for model performance and bias/fairness checks.

Soft Skills :

- Strong communication and stakeholder management.

- Experience guiding Data Scientists through productionization.

- Ability to work on multiple concurrent projects in a fast-paced environment.

Good to Have :

- 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.

- Experience should be 3+ years.