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 workplace 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.

📌 Mlops Engineer (Gurugram)
🏢 EXL
📍 Gurugram