> Markdown version of [/jobs/ext/2990709-ai-platform-engineer](https://www.wearedevelopers.com/jobs/ext/2990709-ai-platform-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # AI Platform Engineer - **Company:** ITQ - **Location:** Rotterdam, Netherlands - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Linux, Open Source Technology, OpenShift, Performance Tuning, Role-Based Access Control, Ansible, VMware VSphere, Cloud Platform System, SUSE Linux, Large Language Models, AI Platforms, Git Flow, Kubernetes, Machine Learning Operations, Hardware Infrastructure, Terraform, Vmware - **Published:** September 19, 2026 - **Apply:** https://www.nationalevacaturebank.nl/vacature/f5ca614c-1ebb-45dc-93e3-aac21b01c79a/ai-platform-engineer ## About the Role You have a strong interest in AI and want to help build the environments where AI can actually be used. Perhaps you come from a background in platform engineering, Kubernetes, or cloud-native infrastructure and want to shift your focus more toward AI. Or maybe you already have experience with AI platforms or MLOps. It's important that you have a solid grasp of the technical fundamentals: + Kubernetes and container orchestration, including deployment, troubleshooting, and day-2 operations + Linux and cloud-native fundamentals such as networking, storage, and security + An IaC mindset, with experience in Ansible, Terraform, and GitOps tooling + A genuine curiosity about how models are served, scaled, and monitored + The ability to work independently in client environments while contributing to a growing AI practice Knowledge of ML or MLOps, experience with model training and fine-tuning, and experience with RAG, LLM serving, agentic frameworks, or GPU infrastructure are a plus. ## Description As an AI Platform Engineer, you'll work on the technical foundation for serious AI applications. You won't be working on a single model, use case, or internal product. Instead, you'll build diverse AI environments across multiple sectors, each with unique requirements for security, compliance, scalability, and governance. You'll ensure that AI doesn't remain stuck at the idea stage but is implemented in environments ready for real-world use. You design, build, and manage infrastructure for AI and machine learning workloads. You work on Kubernetes-based environments for training, deployment, runtime, and operations. In doing so, you use tools such as Kubeflow, MLflow, and ClearML, and work with platforms such as OpenShift AI, SUSE AI, and VMware Private AI Services. You'll work for clients in sectors including government, healthcare, telecom, transportation, and financial services. Sometimes you'll build a new AI environment from the ground up. Sometimes you'll improve an existing landscape. Sometimes you'll ensure that AI tools can be used safely and in a controlled manner within the boundaries of a complex organization. The core remains the same: you make AI workable in production. Examples of projects you can actually build: + For a leading satellite manufacturer in Belgium, we designed and implemented an enterprise Kubernetes platform on vSphere, built on the CNCF open-source stack as a governed foundation for GPU-accelerated AI and ML workloads. Using tools such as Harbor, FluxCD, OPA Gatekeeper, NVIDIA GPU Operator, and NVIDIA AI Enterprise, we made training and inference pipelines production-ready. + For a national public transportation organization, we built a sovereign AI platform on internal Kubernetes, featuring an Agent and MCP Gateway, JWT and CEL-based RBAC, an MCP Registry, and a custom governance UI. This allowed engineers to work with approved AI tools without taking data outside the organization. ## Related Videos - [DevOps for AI: running LLMs in production with Kubernetes and KubeFlow](https://www.wearedevelopers.com/videos/1222-devops-for-ai-running-llms-in-production-with-kubernetes-and-kubeflow) - [Docker network without Docker](https://www.wearedevelopers.com/videos/1418-docker-network-without-docker) - [WebAssembly: The Next Frontier of Cloud Computing](https://www.wearedevelopers.com/videos/972-webassembly-the-next-frontier-of-cloud-computing) - [Dev & Test in the Cloud? Deploy your cloud environments with Ansible & Terraform](https://www.wearedevelopers.com/videos/1607-dev-test-in-the-cloud-deploy-your-cloud-environments-with-ansible-terraform) - [Docker exec without Docker](https://www.wearedevelopers.com/videos/1094-docker-exec-without-docker) - [Generating code with Angular schematics](https://www.wearedevelopers.com/videos/129-generating-code-with-angular-schematics) ## Related Articles - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [Navigating the AI Shift](https://www.wearedevelopers.com/magazine/629-navigating-the-ai-shift) - [MLops – Deploying, Maintaining And Evolving Machine Learning Models in Production](https://www.wearedevelopers.com/magazine/115-mlops-deploying-maintaining-and-evolving-machine-learning-models-in-production) - [Stephan Gillich - Bringing AI Everywhere](https://www.wearedevelopers.com/magazine/489-stephan-gillich-bringing-ai-everywhere)