MLOps Engineer
Core
Build, operate, and evolve a Kubeflow-based ML platform on Azure to enable reliable, scalable, and cost-efficient ML workflows for MLE and Data Science teams.
Role type
Mid-level MLOps Engineer
Builds
CI/CD pipelines, Kubernetes-based ML infrastructure, and observability solutions for model training, inference, and batch pipelines.
Domain
Cloud Infrastructure (Azure) + Machine Learning Operations
Deliverable
production ML models
Required skills
Kubeflow, Kubernetes, Terraform, Python, Azure cloud services (AKS, ACR, Storage, Networking, IAM), CI/CD pipeline construction, ML lifecycle management, observability tooling
Preferred skills
Container image management, cost optimization strategies, documentation creation, cross-team collaboration
Technologies
Kubeflow, Azure Kubernetes Service (AKS), Terraform, Python, GitHub Actions, Azure DevOps, Argo, Prometheus, Grafana, Azure Monitor, DataDog, TensorFlow, PyTorch, scikit-learn
Responsibilities
Deploy and operate Kubeflow components on AKS; Build and maintain CI/CD pipelines for ML workflows; Implement logging, monitoring, and alerting; Diagnose and resolve infrastructure failures; Onboard workflows onto Kubeflow; Identify compute usage optimization opportunities.
Seniority
Mid-level, hands-on IC
