Celebal Technologies

1 week ago

Navi Mumbai, India Celebal Technologies Full time

Job Description :Key Responsibilities :ML Pipeline Design & Automation :- 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.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. (ref:hirist.tech)

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