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,

📌 Mlops Engineer (Distrito del Ensanche)
🏢 Appodeal
📍 Distrito del Ensanche