Sovereign AI Platform Engineer T Cloud Public

operations suitable for sovereignty-sensitive programs Key responsibilities 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. Qualifications 5+ years in platform engineering, DevOps, SRE, or MLOps, with strong Kubernetes and Linux expertise. Proven experience with AI infrastructure, model serving, private or on-prem deployments, and production operations for LLM-based workloads. Strong hands-on skills in Python plus automation tooling such as Terraform, Ansible, Helm, and GitOps workflows. Good understanding of networking, storage, access control, monitoring, and operational hardening in high-security environments. Comfortable working in sovereignty-driven environments where auditability, isolation, and controlled data handling are mandatory. Additional Information What do we offer you? Work environment & flexibility International, dynamic and collaborative environment. T-Social: social initiatives (sports, community, health, …). Hybrid work model (remote/on-site). Flexible working hours. Growth & development Customized training: access to Coursera to learn whatever you want, whenever you want. Weekly language classes (English & German). International Mentoring Sessions & Experience Days. Compensation & benefits Flexible compensation plan (health insurance, meal vouchers, childcare, transport). Telemedicine. Life and accident insurance. Social fund. Wellbeing & time off 26+ working days of vacation per year. Free access to specialist services (medical, legal, wellness). 100% salary coverage during medical leave. And many more advantages of being part of T-Systems If you are looking for a new challenge, do not hesitate to send us your CV Please send CV in English. Join our team T-Systems Iberia will only process the CVs of candidates who meet the requirements specified for each offer.

Company Description T-Systems is part of the Deutsche Telekom Group, with around 30.000 employees worldwide. We create technology with purpose to generate a positive impact on society. We are looking for curious talent, eager to learn, take on challenges, and contribute ideas that transform our customers’ experience. We trust people: we offer autonomy, continuous support, and a collaborative environment where you can grow without limits. We are one global team, guided by respect, integrity, and a passion for doing better every day. Job Description Mission 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