MLOps Engineer

Overview

MLOps Engineer responsible for the end-to-end lifecycle of machine learning models in production. You’ll build infrastructure and operational discipline for reliable, observable, and cost-effective ML and LLM systems.

What you'll do

• Build and maintain end-to-end ML pipelines for training, evaluation, and deployment.
• Own model deployment and serving infrastructure, including containerised endpoints, versioning, traffic management, and rollbacks.
• Implement monitoring and observability with performance monitoring, data drift detection, and alerting.
• Govern LLM usage by managing provider relationships, API access/versioning, token consumption, and cost optimisation.
• Manage the LLM gateway and prompt versioning using tracing/evaluation tooling.
• Support experiment tracking and model registry for reproducibility and auditable experiment-to-production flow.
• Apply CI/CD, infrastructure as code, and automated testing to ML workflows.

What you'll need

• 3-4 years of experience in machine learning and Azure-related deployments/pipelines.
• Experience with Azure-native tooling such as Azure AI Foundry and Azure Machine Learning.

Details

• Location: Bangalore.