Sovereign AI Platform Engineer (DevOps Engineer) (m/f/d)

Purpose

Design, build, and operate a sovereign AI toolchain used in isolated or tightly controlled environments, including model serving, retrieval, orchestration, observability, and secure platform operations for enterprise AI engineering workloads
Role focus
This role centers on open-source or open-weight LLM stacks, air-gapped or isolated deployment models, Kubernetes-based platform engineering, GPU-enabled environments, and auditable AI operations suitable for sovereignty-sensitive programs

WHAT WILL YOU DO?

• Design and run Kubernetes environments optimized for AI inference, retrieval, experimentation, and agent execution in secure or isolated settings
• Deploy and operate open-source or open-weight model stacks, model gateways, vector databases, and supporting platform components
• Build reproducible platform automation using Infrastructure as Code and GitOps approaches for stable, auditable delivery
• Manage local registries, package mirrors, secrets, access controls, storage, networking, and observability in environments with limited or no public cloud dependency.
• Optimize GPU, compute, and storage usage for reliable AI workloads while maintaining security and data sovereignty requirements. Examples of market tools, models, and platform components expected
• Inference and local serving stacks such as vLLM, Ollama, llama.cpp, or OpenAI-compatible self-hosted endpoints.
• Open-source or open-weight models appropriate for sovereign deployment, for example coding-capable and general-purpose families hosted internally through approved serving layers.
• Platform tooling such as Kubernetes, Helm, Terraform, Ansible, ArgoCD, private registries, Qdrant or similar vector stores, and Open WebUI or comparable internal interfaces.
• Developer-facing integration options such as VS Code-compatible extensions, Continue-style local model connectors, or editor integrations pointed at internal APIs instead of external SaaS endpoints.
• Hardware awareness covering GPU-backed nodes, CPU-only fallback options, storage performance, network isolation, and on-prem or dedicated infrastructure patterns.