Job Description

The Senior MLOps Engineer will be responsible for designing, implementing, and managing the end-to-end operational lifecycle of Machine Learning and Generative AI models. This includes model packaging, deployment, CI/CD automation, model monitoring, infrastructure management, governance, observability, and continuous optimization.

The role requires solid expertise in cloud infrastructure, DevOps, Kubernetes, ML platforms, model lifecycle management, and enterprise-scale AI deployment.

The ideal candidate will work closely with AI Engineers, Data Scientists, ML Engineers, AI Platform Engineers, Enterprise Architecture, Infrastructure, Information Security, and Digital Engineering teams to operationalize AI across the enterprise.
MLOps Platform Strategy
Design and implement the enterprise MLOps platform.
Define the operating model for AI and ML deployments.
Establish standards for model lifecycle management.
Develop reusable deployment templates and automation frameworks.
Build scalable AI deployment capabilities across cloud and on-premise settings.




Define engineering best practices for AI operations.
Machine Learning Lifecycle Management

Manage The Complete Lifecycle Of AI/ML Models Including
Model Registration
Version Control
Model Packaging
Model Validation
Model Deployment
Model Promotion
Model Rollback
Model Retirement
Model Archiving

Implement automated governance throughout the ML lifecycle.
CI/CD for AI

Develop Automated CI/CD Pipelines For
Machine Learning Models
Generative AI Applications
AI APIs
Feature Engineering Pipelines
Data Validation Pipelines
Model Testing
Prompt Evaluation
AI Agent Deployments

Integrate deployment pipelines with enterprise DevOps processes.
Production Deployment

Deploy AI Solutions Across Enterprise Platforms Including
Loan Origination Systems
CRM
Mobile Applications
Customer Portals
Contact Centre Platforms
Collections Platforms
Enterprise Da

📌 Chief Manager Mumbai
🏢 HDB Financial Services
📍 Mumbai