Over de functie

Job title: DevOps / AI Infrastructure Engineer — GPU & Kubernetes
Location: 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 summary
Senior 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.

Must‑have skills & qualifications

• 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 with GCP/AWS is valuable).

• Experience with GPU‑accelerated environments and NVIDIA ecosystem.

• Deep understanding of Kubernetes and container orchestration for high‑performance computing and model serving.

• Experience with AI platform tools: NVIDIA Triton, Kubeflow, MLflow (setup, pipelines, serving).

• Strong scripting and programming skills (Python, Bash) for automation and data processing.

• Proficiency with Infrastructure‑as‑Code: Terraform and configuration management with Ansible.

• Solid knowledge of storage, networking and security considerations for large ML workloads.

• Good communication and collaboration skills; able to work with ML researchers and engineering teams.

Preferred skills

• Experience integrating AI/ML workflows with PLM or simulation platforms.

• Familiarity with GPU scheduling solutions (NVIDIA GPU Operator, device plugins, Volcano, etc.).

• Knowledge of monitoring/observability for ML platforms (Prometheus, Grafana, ELK, metrics for GPU workloads).

• Experience with cost‑optimisation and autoscaling strategies for GPU clusters.

• Familiarity with model optimization techniques (quantization, batching, mixed precision) and inference performance tuning.

What we offer

• Work on cutting‑edge AI infrastructure supporting R&D and engineering use cases.

• Opportunity to shape GPU and MLOps practices in a collaborative technical environment.

• Competitive compensation and Eindhoven‑based role with flexible working arrangements.

Wat breng jij als DevOps / AI Infrastructure Engineer — GPU & Kubernetes

Wat bieden wij vanuit ITproposal B.V.