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

Work arrangement
No