ML Ops-Model Deployment & Monitoring

Greetings

We have a few requirements for ML Ops. PFB the details

Key Responsibilities

• Partner with data scientists to package models and prepare them for production deployment
• Deploy models for batch scoring or real-time inference based on business requirements
• Develop production scoring and supporting data pipelines using Python and PySpark
• Automate deployment validation, versioning and release workflows
• Manage model artifacts, dependencies, configurations and deployment history
• Monitor model performance, data drift, scoring failures and operational reliability
• Implement alerts, troubleshooting procedures, rollback mechanisms and recovery processes
• Support approved model refreshes and redeployment in collaboration with model owners
• Work with technology, security and model-governance teams to meet enterprise requirements
• Maintain deployment documentation, monitoring controls and operational runbooks

Required Skills & Experience

• Strong hands-on proficiency in Python and PySpark
• Practical experience operationalizing and deploying machine-learning models.
• Sound modeling knowledge, including evaluation metrics, model behaviour, drift and performance interpretation
• Experience building production scoring pipelines and monitoring model operations
• Familiarity with model versioning and lifecycle-management tools
• Experience with CI/CD, automated testing and dependency management
• Ability to troubleshoot issues across data pipelines, model execution and deployment environments
• Working knowledge of the banking domain and the governance requirements associated with production models

Preferred Skills

• Experience with Databricks and Azure-based deployment environments
• Familiarity with APIs, containers and orchestration platforms
• Experience supporting model approvals, audit documentation and controlled releases