Role & responsibilities
Deploy, operationalize and maintain ML models in production, on cloud and on-premises.
Build end-to-end ML pipelines covering data prep, training, validation, deployment and monitoring.
Develop feature and data pipelines in Python and PySpark.
Work on Snowflake and/or Databricks for model development, scoring and data processing.
Implement CI/CD for ML: versioning, automated testing and release management.
Monitor model performance, data drift and system health, and drive retraining and remediation.
Work with data scientists, data engineers, architects and business stakeholders to productionize models.
Ensure security, governance, auditability and compliance for the Banking domain.
Document architectures and deployment procedures, and follow reproducibility best practices.

Preferred candidate profile




Robust hands-on experience as an MLOps Engineer deploying ML models in production.
Proficient in Python and PySpark.
Working experience with Snowflake and/or Databricks.
Experience deploying models in cloud and/or on-premises environments.
Solid understanding of ML lifecycle management, model monitoring and CI/CD for ML.
Valuable analytical, problem-solving and communication skills.
Positive to have:
Banking or financial services experience.
Containerization and orchestration tools (e.g., Docker, Kubernetes).
Model registry and experiment tracking tools (e.g., MLflow).
Understanding of model risk management and banking regulations.

📌 Mlops Engineer Gurugram (India)
🏢 Crescendo Global Leadership Hiring India
📍 India