Solutions Architect (AI/ML)

TL;DR

Solutions Architect (AI/ML): Designing and implementing cutting-edge cloud infrastructure solutions for AI/ML teams with an accent on GPU orchestration, MLOps pipelines, and scalability. Focus on acting as a trusted technical advisor, optimizing cloud resource utilization, and bridging the gap between complex AI infrastructure and client business requirements.

Location: Must be based in the United Kingdom

Company

Nebius is a leading full-stack AI cloud platform provider, building infrastructure for the global AI economy from data training to production deployment.

What you will do

• Act as a trusted technical advisor to clients, conducting workshops, PoCs, and training on GPU cloud technologies.
• Design and document Infrastructure as Code (IaC) solutions tailored to client business requirements.
• Collaborate with support engineers and technical writers to create high-quality technical documentation.
• Optimize pipeline performance and scalability to ensure efficient utilization of Nebius AI cloud resources.
• Serve as the primary point of expertise for customer scenarios across product, technical support, and marketing teams.
• Support marketing efforts during industry events, hackathons, and webinars.

Requirements

• 5+ years of experience as a cloud solutions architect, system/network engineer, or developer.
• Strong hands-on experience with IaC tools (Terraform/Ansible), Kubernetes, and Python.
• Solid understanding of GPU computing practices for ML training and inference, including drivers and libraries like CUDA.
• Must be authorized to work in the United Kingdom.
• Excellent communication skills and a customer-centric mindset.

Nice to have

• Experience with HPC/ML orchestration frameworks like Slurm or Kubeflow.
• Proficiency with deep learning frameworks such as TensorFlow or PyTorch.
• Knowledge of the cloud ML tools landscape (NVIDIA, AWS, Azure, Google).

Culture & Benefits

• Competitive compensation package.
• Opportunities for career growth and continuous learning.
• Commitment to flexibility and work-life balance.
• Collaborative, innovative, and international work environment.
• Opportunity to work on high-impact AI projects.