Solutions Architect

Role description

Role Overview

We are seeking a highly skilled and customer-focused professional to join our team as a Solutions Architect specializing in Cloud infrastructure and MLOps. As a Cloud Solutions Architect, you will play a pivotal role in designing and implementing cutting-edge solutions for our clients, leveraging cloud technologies for ML/AI teams and becoming a trusted technical advisor for building their pipelines.

What You Will Do

Your responsibilities will include: acting as a trusted advisor to our clients, providing technical expertise and guidance throughout the engagement; conducting PoC, workshops, presentations, and training sessions to educate clients on GPU cloud technologies and best practices; collaborating with clients to understand their business requirements and develop solution architecture that align with their needs; helping customers to optimize pipeline performance and scalability to ensure efficient utilization of cloud resources and services powered by Nebius AI.

Why It Might Be a Fit

We expect you to have 5 - 10 + years of experience as a cloud solutions architect, system/network engineer, developer or a similar technical role with a focus on cloud computing; strong hands-on experience with IaC and configuration management tools (preferably Terraform/Ansible), Kubernetes, skills of writing code in Python; solid understanding of GPU computing practices for ML training and inference workloads, GPU software stack components, including drivers, libraries (e.g. CUDA, OpenCL); excellent communication skills; customer-centric mindset.

Requirements

• 5 - 10 + years of experience as a cloud solutions architect, system/network engineer, developer or a similar technical role with a focus on cloud computing
• Bachelor’s degree or foreign equivalent in a related field, or an equivalent combination of education and relevant experience
• Strong hands-on experience with IaC and configuration management tools (preferably Terraform/Ansible), Kubernetes, skills of writing code in Python
• Solid understanding of GPU computing practices for ML training and inference workloads, GPU software stack components, including drivers, libraries (e.g. CUDA, OpenCL)
• Excellent communication skills
• Customer-centric mindset

Benefits

• Competitive compensation
• Career growth and learning opportunities
• Flexibility and ownership
• Collaborative and innovative culture
• Opportunity to work on impactful AI projects
• International environment and talented teams