Mlops engineer

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

• Strong 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.