Solutions Architect (AI/MLOps)

TL;DR

Solutions Architect (Cloud Infrastructure/MLOps): Designing and implementing cutting-edge cloud solutions for ML/AI teams with an accent on GPU orchestration and MLOps pipelines. Focus on optimizing pipeline performance, developing Infrastructure as Code solutions, and acting as a technical advisor for high-scale AI workloads.

Location: Must be based in a DACH country (Germany, Austria, or Switzerland)

Company

Nebius is building a full-stack AI cloud platform that enables developers and enterprises to train and deploy AI/ML models without the complexity of in-house infrastructure.

What you will do

• Act as a technical advisor to clients, conducting PoCs, workshops, and training on GPU cloud technologies.
• Design and document Infrastructure as Code (IaC) solutions tailored to specific client business requirements.
• Optimize pipeline performance and scalability to ensure efficient use of Nebius AI cloud resources.
• Serve as the primary point of expertise for customer scenarios, collaborating with product, support, and marketing teams.
• Support marketing initiatives during hackathons, conferences, and webinars.

Requirements

• 5+ years of experience as a cloud solutions architect, system/network engineer, or developer.
• Hands-on proficiency with Terraform, Ansible, Kubernetes, and Python.
• Solid understanding of GPU computing practices (CUDA, OpenCL) for ML training and inference workloads.
• Fluency in German or French is preferred.
• Must be authorized to work in the country of application.

Nice to have

• Experience with HPC/ML orchestration frameworks such as Slurm or Kubeflow.
• Hands-on experience with deep learning frameworks like PyTorch or TensorFlow.
• Knowledge of the cloud ML tools landscape from providers like NVIDIA, AWS, Azure, or Google.

Culture & Benefits

• Competitive compensation and structured career growth opportunities.
• Flexibility and a strong focus on work-life balance.
• Collaborative and innovative international environment with talented teams.
• Opportunity to work on impactful AI projects and shape the future of AI infrastructure.