Overview
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 adecuado 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 & Automation:
Build and maintain CI/CD pipelines for ML workflows and platform services
Automate model training, validation, and deployment pipelines
Implement reproducibility and versioning for data, models, and pipelines




Observability & Reliability:
Implement logging, monitoring, and alerting at the platform level
Diagnose and resolve workflow, pipeline, and infrastructure failures
Support SLAs and reliability objectives for ML platforms
Collaboration & Enablement:
Work closely with MLEs and Data Scientists to onboard workflows onto Kubeflow
Provide best practices, templates, and documentation for ML teams
Collaborate with Infra and Security teams on access control and compliance needs
Cost Awareness & Optimization:
Assist with collecting and reporting costs at Kubeflow namespace or workflow level
Identify optimization opportunities related to compute usage and scheduling
Qualifications
3–6 years of experience in MLOps, DevOps, or Platform Engineering
Hands-on experience with Kubeflow, Kubernetes, Terraform (IaC), and containerized ML workloads
Strong experience with Azure cloud services (AKS, ACR, Storage,

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