Software engineer

Job title: DevOps / AI Infrastructure Engineer - GPU & KubernetesLocation: Eindhoven, Netherlands (TNDL - Eindhoven)Start: ASAP (or as agreed)Duration: 6 months (with possibility to extend)Experience: 8-10 years (including at least 1.5 years in DevOps/cloud/SRE focused on AI/ML)Language: English (fluent) Role summarySenior DevOps / AI Infrastructure Engineer to design, build and operate GPU-accelerated AI/ML infrastructure. You will enable high-performance training and inference workflows by managing cloud/GPU platforms, Kubernetes clusters, IaC, and AI tooling (Triton, Kubeflow, MLflow). The role combines deep platform engineering with automation and close collaboration with ML engineers and R&D teams. Key responsibilities

Design, deploy and operate GPU-enabled Kubernetes clusters and associated platform services for training and inference.

Build and maintain CI/CD, model CI and MLOps pipelines using tools such as Kubeflow, MLflow and Triton.

Implement and manage cloud infrastructure on Azure (and other clouds as needed), with GPU instances and storage for large datasets.

Automate provisioning and configuration using Terraform, Ansible and scripting (Python, Bash).

Optimize container orchestration, scheduling and GPU utilization for high-performance workloads.

Integrate AI inference platforms (NVIDIA Triton) and support model serving at scale.

Work with PLM/simulation and data teams to integrate model training and inference into engineering workflows.

Monitor, troubleshoot and tune platform performance, reliability and cost.

Define and enforce best practices for security, resource governance and data handling in AI pipelines.

Document architectures, runbooks and operational procedures; transfer knowledge to engineering teams.

8-10 years industry experience; minimum 1.5 years in DevOps, cloud engineering or SRE with AI/ML focus.

Hands-on experience with major cloud providers (Azure preferred; experience wi.