Senior Kubernetes Engineer

GTN Solutions GmbH; Dallas (TX); Hybrid; USD 140,000 - 180,000; Full time

Benefits offered by this job

Relocation assistance

Company-paid benefits

Remote work available

Job summary

GTN Solutions GmbH is seeking a Senior Kubernetes Engineer in Dallas, TX (Hybrid). You will design and scale a GPU-accelerated compute platform, building high-throughput, multi-tenant container environments across on-prem and hybrid setups. This hands-on role requires ownership of performance, automation, and platform evolution.

You will collaborate with platform, HPC, and ML teams to deliver reliable Kubernetes clusters and GPU workloads at scale in a fast-growing infra landscape.

Qualifications

• Experience designing and operating large-scale Kubernetes clusters.
• Strong background in GPU-accelerated compute platforms for AI/HPC.
• Hands-on implementation of high-throughput, multi-tenant container environments.

Responsibilities

• Design and scale a GPU-accelerated compute platform in hybrid on-prem and cloud.
• Architect and operate clusters with a focus on performance and reliability.
• Collaborate with platform, HPC, and ML engineering teams to deliver multi-tenant compute at scale.

Skills

Cloud architecture

System design

Performance optimization

Tools

Kubernetes

Docker

GPU acceleration

Job description

Senior Kubernetes Engineer Location: Dallas, TX (Hybrid - 3/2) | Relocation available | Remote available Type: Direct Hire - Competitive base salary + performance bonus - 100% company-paid benefits

We are seeking a Senior Kubernetes Engineer to help design and scale a next-generation GPU-accelerated compute platform supporting AI, machine learning, and high-performance computing workloads. This role sits at the core of a rapidly expanding infrastructure environment, focused on building high-throughput, highly efficient container platforms across on-prem and hybrid environments. You will play a key role in architecting and operating large-scale Kubernetes clusters optimized for GPU workloads, working closely with platform, HPC, and ML engineering teams to deliver reliable, multi-tenant compute at scale. This is a hands-on engineering role with strong ownership across performance, automation, and platform evolution.