Job Description:

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.