MLOps Engineer

Role Overview

We are looking for an experienced MLOps Engineer to build and scale our AI infrastructure across Kubernetes, cloud-native environments, and serverless GPU platforms. You will own the end-to-end operational lifecycle of machine learning models—from training to deployment, monitoring, optimization, and automated retraining.

Key Roles

• Design and implement highly scalable AI / ML infrastructure using Kubernetes, Kubeflow, Ray, and cloud-native services.
• Build robust CI / CD and CT (Continuous Training) pipelines for model deployment, inference, monitoring, and automated retraining.
• Architect and deploy ML workflows on serverless GPU platforms (AWS, GCP, Yotta, RunPod, Modal, etc.) for cost-efficient, elastic scaling.
• Establish automated systems for model drift, data drift, performance monitoring, and lineage tracking.
• Promote best practices in reproducible ML, infrastructure-as-code, automation, and internal tooling.

Responsibilities

Knowledge & Skills Requirements