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.
- Good analytical, problem-solving and communication skills.

- Good 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)
🏢 Crescendo Global Leadership Hiring India
📍 Gurugram