Senior DevOps Engineer + 3 000 € Sign‑On Bonus - Senior customer facing position for Industrial AI Cloud (m/f/d)

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Purpose NVIDIA and Deutsche Telekom are jointly developing industrial AI cloud for Europe. This AI factory in Germany will host 10,000 GPUs across NVIDIA DGX B200 systems and RTX Pro Servers. Deutsche Telekom provides secure, sovereign and fast infrastructure, including data centers, operations, security, and AI solutions. Role Overview: DevOps Engineer (Senior) / AI Consultant Customer Facing Engineer you will guide enterprise customers through onboarding, training, and early adoption of the AI platform. Your responsibility includes understanding customer requirements, supporting solution design, executing Proofs of Concept (PoCs), and ensuring smooth integration of customer workloads (LLMs, GPU compute, AI pipelines). You act as a trusted technical advisor, helping customers efficiently use their GPU clusters and AI toolchains. WHAT WILL YOU DO? Consult customers on all technical aspects related to GPU infrastructure, AI/ML model training, and platform usage. Lead onboarding and training, mentoring customer specialists on optimal usage of their GPU clusters and AI environments. Design and implement PoCs, including environment setup, data processing pipelines, and deployment workflows. Conduct requirement engineering, translating business needs into technical specifications. Assist customers with performance optimization, troubleshooting, fine‑tuning, and validation of delivered solutions. Act as the key technical point of contact, coordinating cross‑functional teams across infrastructure, networking, automation, security, and AI services. Propose and develop automation concepts to improve services, processes, and operating models. Ensure best practices in reliability, scalability, responsible AI, and security are applied across the customer lifecycle. Support monitoring, observability, and capacity planning for AI workloads and GPU utilization. YOU WILL SUCCEED IF YOU: Technical Background Have Master's degree in information technology, Computer Engineering, Applied AI, or related field. Possess strong knowledge of NVIDIA GPUaccelerated platforms (DGX, B200, RTX Pro Servers). Are experienced in running and training selfhosted LLMs, including model finetuning and inference optimization. Possess hands-on experience with Slurm, Run:AI, or other GPU workload schedulers. Have advanced Linux administration skills. Have solid understanding of Kubernetes and containerized AI workflows. Are proficient in scripting (Python, Bash) for automation, data manipulation, and tooling. Have experience with Infrastructure as Code (Ansible, Terraform, Helm). Possess knowledge of SoftwareDefined Networking (SDN) and highperformance network architectures. Have experience with monitoring and visualization tools (Prometheus, Grafana, Alert manager). Have experience with Data Engineering/Transformation/Migration tools and pipelines. Highly Valuable Knowledge (AISpecific) Understand LLM architectures, embeddings, and vector databases. Are familiar with RAG pipelines, model evaluation, and prompt engineering. Have knowledge of responsible AI practices (security, governance, compliance). Have experience with AI/ML frameworks: PyTorch, TensorFlow, Hugging Face, Triton Inference Server. Soft Skills & Other Requirements Speak English at C1 level; German is an advantage. Possess strong customer facing communication skills, both technical and non-technical. Have basic experience with requirement engineering. Have experience with software testing, quality assurance, and validation on intermediate level. Have analytical mindset, problem solving skills, structured approach to troubleshooting. Are able to work independently as well as coordinate with cross-functional teams.

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