Mlops engineer

Crescendo Global Leadership Hiring India; Gurugram District; Hybrid; INR 1,800,000 - 2,800,000; Full time

Job summary

Crescendo Global Leadership Hiring India seeks an experienced MLOps Engineer to deploy and maintain ML models in production across cloud and on-prem environments. The role involves building end-to-end pipelines with Python and PySpark, and collaborating with data teams to productionize models.

The candidate should have hands-on MLOps experience, strong Python/PySpark skills, and familiarity with Snowflake or Databricks.

Qualifications

• Must have hands-on experience deploying ML models in production.
• Proficient in Python and PySpark.
• Experience with Snowflake and/or Databricks.
• Experience deploying models in cloud and/or on-prem environments.
• Strong understanding of ML lifecycle management and CI/CD for ML.

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.

Skills

MLOps

Python

PySpark

CI/CD for ML

Model monitoring

Cloud/on-prem

Communication

Tools

Docker

Kubernetes

MLflow

Snowflake

Databricks

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.