MLOps and ML platform jobs in Bengaluru

MLOps engineers build the platforms that let data scientists and ML engineers train, deploy and monitor models without reinventing the plumbing each time. The role sits between DevOps and machine learning, and has grown with the cost and complexity of running large models on GPUs.

In Bengaluru these roles are at companies with many models in production, at AI startups serving models to customers, and at cloud and tooling vendors.

What the work involves

• Build pipelines for training, evaluation and deployment of models.
• Run model serving at scale, including GPU scheduling and autoscaling.
• Track experiments, datasets and model versions so results can be reproduced.
• Monitor models in production for latency, cost and drift.

Skills employers look for

• Kubernetes and containers
• Python
• MLflow, Kubeflow or a managed ML platform
• Model serving (vLLM, Triton, TorchServe)
• GPU infrastructure
• CI/CD for models
• Cloud (AWS SageMaker, Vertex AI or Azure ML)

How interviews usually run

Expect DevOps-style rounds on Kubernetes, cloud and debugging, a coding round, and a design round such as building a platform where teams can deploy models with one command, or serving a large language model cheaply at high traffic.

Interviewers check that you understand the ML side well enough to talk to data scientists: what training needs, why models degrade, and how evaluation works.

How to stand out

• Show numbers from platforms you ran: deployment time, GPU utilisation, cost per prediction.
• Hands-on experience serving large language models is in high demand.
• A Kubernetes or cloud ML certification helps at screening.