About the Role
We are seeking a Mid-Level MLOps Engineer to build, operate, and evolve our Kubeflow-based ML platform on Azure. This role focuses on enabling reliable, scalable, and cost-efficient ML workflows by designing CI/CD pipelines, managing Kubernetes-based ML infrastructure, improving platform observability, and supporting MLE and Data Science teams across the model lifecycle.
The ideal candidate is hands-on, comfortable working across infrastructure and ML workflows, and motivated to operationalize best practices in MLOps.
Responsibilities
Platform & Infrastructure:
Deploy, configure, and operate Kubeflow components on Azure Kubernetes Service (AKS)
Support Kubernetes workloads for training, inference, and batch pipelines
Manage container images, registries, and ML runtime environments
Assist with Kubeflow and Kubernetes upgrades under senior guidance
CI/CD & Autom...
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