Job description

About Nebius: Nebius is leading a new era in cloud infrastructure for the global AI economy. We are building a full-stack AI cloud platform that supports developers and enterprises from data and model training through to production deployment, without the cost and complexity of building large in-house AI/ML infrastructure. Built by engineers, for engineers. From large-scale GPU orchestration to inference optimization, we own the hard problems across compute, storage, networking and applied AI. Listed on Nasdaq (NBIS) and headquartered in Amsterdam, we have a global footprint with R&D hubs across Europe, the UK, North America and Israel. Our team of 1,500+ includes hundreds of engineers with deep expertise across hardware, software and AI R&D. The role As a Solutions Architect, Enterprise, you will be the technical lead for a portfolio of large customers evaluating, adopting and expanding Nebius AI Cloud. You will connect each customer's AI strategy and business priorities to a secure, scalable architecture, then guide the customer from technical discovery through technical scoping and production adoption. This role sits at the intersection of cloud architecture, AI infrastructure and business value. You will work with Enterprise sales leaders and engage executives, platform teams, infrastructure leaders, security stakeholders and AI or machine learning practitioners. The strongest candidates combine hands-on technical depth with the judgment to navigate complex decisions of large organizations in real economic sectors. You’re welcome to work remotely from the USA. Your responsibilities will include: Serve as the trusted technical advisor for Enterprise accounts throughout evaluation, onboarding, adoption and expansion. Lead technical discovery with business and technology stakeholders, clarify success criteria and translate requirements into practical AI cloud architectures. Design solutions across Nebius product portfolio, with clear attention to Enterprise value proposition. Plan and deliver architecture workshops, executive briefings, demonstrations, proofs of concept and technical enablement sessions. Develop reference architectures, deployment patterns and infrastructure-as-code examples that help customers move from evaluation to production. Build business cases with sales and customer stakeholders, navigating through complex decision-making processes in Enterprise. Coordinate with Sales, Product, Engineering, Support and Customer Experience to resolve risks, define ownership and maintain momentum across complex engagements. Represent the voice of Enterprise customers internally and provide specific feedback that improves product priorities. We expect you to have: 7+ years of experience in solutions architecture, cloud architecture, platform engineering, systems engineering or a comparable customer-facing technical role. Proven track record supporting Enterprises and a working understanding of how they evaluate, purchase, govern, deploy and expand strategic technology platforms. Excellent presentation, facilitation and stakeholder-management skills, including confidence with CIOs, CTOs, infrastructure leaders, AI leaders and business sponsors. Strong knowledge and hands on experience with cloud-native infrastructure, including compute and orchestration, storage, networking, security, identity and Kubernetes. Understanding of Enterprise AI adoption lifecycle and success criteria associated with large-scale training and inference workloads. Willingness to travel as customer and business needs require. It would be an added bonus if you have: Production experience with GPU platforms, accelerated computing, CUDA or high-performance 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). Experience evaluating the total cost of ownership of large-scale compute or GPU workloads. Pay Transparency We offer competitive compensation and benefits packages. Actual compensation will be determined based on job-related factors, including experience, skills, qualifications, the level at which the candidate is hired, and geographic location, consistent with applicable law. On Target Earnings Range $224,400 — $326,300 USD Benefits & Perks: 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 What's it like to work at Nebius: Fast moving - Bold thinking - Constant growth - Meaningful impact - Trust and real ownership - Opportunity to shape the future of AI Equal Opportunity Statement: Nebius is an equal opportunity employer. We are committed to fostering an inclusive and diverse workplace and to providing equal employment opportunities in all aspects of employment. We do not discriminate on the basis of race, color, religion, sex (including pregnancy), national origin, ancestry, age, disability, genetic information, marital status, veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by applicable law. Applicants must be authorized to work in the country in which they apply and will be required to provide proof of employment eligibility as a condition of hire. If you need accommodations during the application process, please let us know.

Requirements

Must-haves

Serve as the trusted technical advisor for Enterprise accounts throughout evaluation, onboarding, adoption and expansion.
Lead technical discovery with business and technology stakeholders, clarify success criteria and translate requirements into practical AI cloud architectures.
Design solutions across Nebius product portfolio, with clear attention to Enterprise value proposition.
Plan and deliver architecture workshops, executive briefings, demonstrations, proofs of concept and technical enablement sessions.
Develop reference architectures, deployment patterns and infrastructure-as-code examples that help customers move from evaluation to production.
Build business cases with sales and customer stakeholders, navigating through complex decision-making processes in Enterprise.
Coordinate with Sales, Product, Engineering, Support and Customer Experience to resolve risks, define ownership and maintain momentum across complex engagements.
Represent the voice of Enterprise customers internally and provide specific feedback that improves product priorities.
7+ years of experience in solutions architecture, cloud architecture, platform engineering, systems engineering or a comparable customer-facing technical role.
Proven track record supporting Enterprises and a working understanding of how they evaluate, purchase, govern, deploy and expand strategic technology platforms.
Excellent presentation, facilitation and stakeholder-management skills, including confidence with CIOs, CTOs, infrastructure leaders, AI leaders and business sponsors.
Strong knowledge and hands on experience with cloud-native infrastructure, including compute and orchestration, storage, networking, security, identity and Kubernetes.
Understanding of Enterprise AI adoption lifecycle and success criteria associated with large-scale training and inference workloads.
Willingness to travel as customer and business needs require.
Production experience with GPU platforms, accelerated computing, CUDA or high-performance 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).
Experience evaluating the total cost of ownership of large-scale compute or GPU workloads.
Fast moving - Bold thinking - Constant growth - Meaningful impact - Trust and real ownership - Opportunity to shape the future of AI

Nice-to-haves

It would be an added bonus if you have:

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aimlmachine learningkubernetespython

Platform: greenhouse
Effort: quick