Job Description
Description
Build the AI environments that organizations can actually use
Many AI roles stop at a model, an experiment, or a demo. At ITQ, the work actually begins after that. We build the environments in which AI can run securely, scalably, and manageably. Not isolated proof-of-concepts, but mature AI platforms for organizations that take their data, security, and continuity seriously. Think of environments for LLMs, model training, inference, and governance, designed for production.
Organization
At ITQ, we’re building an AI capability that’s growing rapidly and becoming increasingly sophisticated. We combine technologies like OpenShift AI, SUSE AI, and VMware Private AI Services into a single Private AI approach, with a focus on sovereignty, governance, and operational control.
You’re joining at a time when AI is still in full flux, so you’ll not only be contributing but also helping to shape how we move forward—in terms of content, technology, and as a team.
The Role
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.
Requirements
What do you bring to the table?
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.
What you’ll get
You’ll join a growing team where AI is truly a strategic focus. You’ll work on concrete AI projects across multiple sectors and have ample opportunity to develop your expertise.
You can count on a robust onboarding program, challenging projects, access to the AI Lab, a Claude subscription, opportunities for certification and professional development, a competitive salary, excellent benefits, and a hybrid work model based on ITQ.
Ready for What’s Next?
Do you want to build AI environments that not only sound smart, but are also truly usable, secure, and scalable?
Then we’d love to get to know you. And let’s show you what’s already up and running, what we’re building now, and where you’ll fit in.
