Principal DevOps Engineer for Industrial AI Cloud

• Customized training: access to Coursera to learn whatever you want, whenever you want.
• Weekly language classes (English, Spanish & German).
• International Mentoring Sessions & Experience Days.
• Flexible compensation plan (health insurance, meal vouchers, childcare, transport).
• Telemedicine.
• Life and accident insurance.
• Social fund.
• 26+ working days of vacation per year.
• Free access to specialist services (medical, legal, wellness).
• 100% salary coverage during medical leave.

• Have 3+ years of experience in the design and delivery of systems based on IaaS, PaaS and SaaS.
• Have experience with GPU based infrastructure.
• Possess solid knowledge of Kubernetes container-based technologies.
• Have expert knowledge of scripting languages (Python/Bash).
• Possess expert knowledge of Automation tools and automation deployments (Ansible/Salt-stack/Terraform/Helm).
• Have expert knowledge of CI/CD in a Kubernetes environment and repository management.
• Are expert in automation with Git (GitHub, GitLab) and CI/CD tools like GitHub Actions or GitLab CI/CD.
• Have strong experience with Linux OS.
• Have experience with monitoring and visualization tools (Grafana, Prometheus,…)

Other skills:

• Good communication skills, analytical thinking, team cooperation, presentation skills, negotiation skills.
• Project Management- Basic.
• Leadership skills- Basic.
• Quality management- Intermediate.

• Speak English at B2 level (German is an advantage).

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.
As DevOps Engineer Principal 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.

We are looking for a DevOps Engineer Principal position who will play a key role in helping our enterprise customers successfully adopt and scale our AI platform.
In this position, you will guide clients through onboarding, training, and early-stage implementation of cutting-edge AI solutions. You’ll work closely with them to understand their technical and business needs, support the design of tailored architectures, and lead Proofs of Concept that validate real-world value. You will ensure smooth integration of complex workloads — from LLM deployment and GPU compute optimization to building end-to-end AI/ML pipelines. As a trusted technical advisor, you will empower customers to use their GPU clusters and AI toolchains efficiently, troubleshoot challenges, and adopt best practices that accelerate their AI journey.

What will you do?

• Consult customers on all technical aspects related to GPU infrastructure 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, and security are applied across the customer lifecycle.
• Support monitoring, observability, and capacity planning for AI workloads and GPU utilization.

• International, dynamic and collaborative environment.
• T-Social: social initiatives (sports, community, health,...).
• Hybrid work model (remote/on-site).
• Flexible working hours.