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

- Robust 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

📌 ML Ops-Model Deployment & Monitoring (Gurugram)
🏢 Pineswift Technologies
📍 Gurugram