Solutions Architect – Enterprise

This is a fully remote position, open to applicants in United States.

📋 Description

• Act as the reliable technical consultant for Enterprise accounts during evaluation, onboarding, adoption, and expansion phases.

• Lead technical discovery sessions with business and technology stakeholders, define success criteria, and convert requirements into actionable AI cloud architectures.

• Create solutions across the Nebius product suite while emphasizing the value proposition for Enterprises.

• Organize and execute architecture workshops, executive briefings, demonstrations, proofs of concept, and technical enablement sessions.

• Develop reference architectures, deployment strategies, and infrastructure-as-code samples to assist customers in transitioning from evaluation to production.

• Collaborate with sales and customer stakeholders to build business cases through intricate Enterprise decision-making processes.

• Liaise with Sales, Product, Engineering, Support, and Customer Experience teams to mitigate risks, clarify responsibilities, and sustain momentum across complex projects.

• Advocate for the voice of Enterprise customers within the organization and provide insights that enhance product priorities.

⛳️ Requirements

• Over 7 years of experience in solutions architecture, cloud architecture, platform engineering, systems engineering, or a similar customer-facing technical role.

• Demonstrated success in supporting Enterprises and an understanding of their processes for evaluating, purchasing, governing, deploying, and expanding strategic technology platforms.

• Outstanding presentation, facilitation, and stakeholder management abilities, with a strong command when interacting with CIOs, CTOs, infrastructure leaders, AI leaders, and business sponsors.

• Comprehensive knowledge and practical experience with cloud-native infrastructure, including compute and orchestration, storage, networking, security, identity, and Kubernetes.

• Familiarity with the Enterprise AI adoption lifecycle and the success criteria related to large-scale training and inference workloads.

• Willingness to travel as required by customer and business needs.

• Practical experience with GPU platforms, accelerated computing, CUDA, or high-performance computing.

• Hands-on experience with Infrastructure as Code (IaC) and configuration management tools, preferably Terraform/Ansible, Kubernetes, and Python.

• Knowledge of GPU computing practices for machine learning training and inference workloads as well as GPU software stack components, including drivers and libraries like CUDA and OpenCL.

• Experience in evaluating the total cost of ownership for large-scale compute or GPU workloads.

• Applicants must be eligible to work in the country where they apply and provide proof of employment eligibility as a hiring condition.

🏝️ Benefits

• Competitive compensation.

• Opportunities for career advancement and professional development.

• Flexibility and a sense of ownership.

• A collaborative and innovative company culture.

• The chance to engage in impactful AI projects.

• An international work environment with talented teams.