Cloud Solution Architect

Responsibilities

• Educational content creation
• Create technical content demonstrating how to effectively use computing workloads with VMs, GPU clusters, k8s, SLURM, Soperator, etc
• Develop sample code, tutorials, and reference architectures showcasing best practices for cloud computing and ML infrastructure
• Create video tutorials and live coding sessions demonstrating effective use of Nebius cloud infrastructure
• Helping Academy's partners build cloud solutions
• Collaborate with academic partners to understand their requirements and develop solution architectures that align with their needs
• design Infrastructure as Code solutions, documentation, and technical how-to guides in collaboration with the Nebius Solutions Architect Team
• Act as a trusted advisor to our academic partners, providing technical expertise on GPU cloud technologies and best practices

Requirements

• Technical knowledge and skills
• Strong understanding of cloud infrastructure and distributed computing principles
• Experience with virtual machines, containerization, and managing compute resources
• Experience building with Ia C solutions, preferably Terraform
• Knowledge of GPU clusters and techniques for optimizing ML workloads
• Working knowledge of container orchestration systems like Kubernetes and job schedulers like SLURM
• Familiarity with infrastructure components including networking, storage optimization, and resource management
• Experience optimizing performance of diverse workloads in cloud environments
• Strong programming skills, particularly in Python, and familiarity with the Py Torch ecosystem
• Understanding of cloud infrastructure concepts and deployment patterns
• Excellent written communication skills and ability to clearly express technical ideas in text
• Practical experience: 2+ years of experience in software development, cloud engineering, Dev Ops, or a similar technical role
• Demonstrated experience with cloud technologies and infrastructure
• Previous work with infrastructure-as-code, containerization, and cloud environments

2+ years of experience in software development, cloud engineering, Dev Ops, or a similar technical role, Experience with cloud technologies and infrastructure, Experience with infrastructure-as-code and containerization, Strong Python skills and Py Torch ecosystem familiarity, Knowledge of GPU clusters and ML workloads, Experience with Kubernetes and job schedulers like SLURM, Virtual machines, containerization, and compute resource management

• Optional Experience
• Experience with MLflow, Apache Airflow, or Kubeflow
• Familiarity with cloud ML platforms like AWS, GCP, Azure ML, or NVIDIA NGC
• Experience managing hybrid cloud or on-prem GPU infrastructure
• Background working with technology partners and integrating third-party solutions
• Public presentation skills

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, 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.

Fast moving - Bold thinking - Constant growth - Meaningful impact - Trust and real ownership - Opportunity to shape the future of AI

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