Mlops Engineer

About the Role

We are looking for hands-on MLOps Engineers with proven 4 -10 years of experience in deploying and operationalizing ML models in production and managing the ML model lifecycle across cloud and on-premises environment in Banking Domain.

Responsibilities

• Production Monitoring, Logging, Alerting, Observability, Troubleshooting
• Code conversion/refactoring, UAT testing, PIV, and production release activities
• Containerizing and deploying ML predictive models on cloud and on-premises production environments
• Building automated data ingestion pipelines with DQ checks, and orchestrating scoring pipelines with output DQ checks.
• Collaborating with cross-functional teams, including Model Development, Data Engineering, Infrastructure

Qualifications

• 4 -10 years of experience in deploying and operationalizing ML models in production and managing the ML model lifecycle across cloud and on-premises environment in Banking Domain.

Required Skills

• Python & PySpark
• AWS Cloud (SageMaker, EC2, S3, Lambda, EKS, or similar services)
• Good to have: Azure, GCP
• Snowflake, Databricks
• CI/CD Pipelines (Jenkins, GitHub Actions, GitLab CI/CD)
• Docker & Kubernetes
• GitHub, GitLab, Release Management
• MLflow, Model Registry, Model Deployment, Airflow, Scheduled Batch Scoring
• Production Monitoring, Logging, Alerting, Observability, Troubleshooting & Rollback Strategies

Preferred Skills

• Code conversion/refactoring, UAT testing, PIV, and production release activities
• Containerizing and deploying ML predictive models on cloud and on-premises production environments
• Building automated data ingestion pipelines with DQ checks, and orchestrating scoring pipelines with output DQ checks.