Educational Content Author - Cloud Solution Architect, Nebius Academy

The Role

Nebius is seeking a Middle AI Solution Architect to drive adoption of our high-performance cloud infrastructure by creating educational content. You’ll join our Nebius Academy team with a mission to help developers succeed with Nebius’ cloud offerings. In this role, you’ll focus on communicating technical capabilities and showcasing differences across Nebius products: cloud infrastructure services and Token Factory.

This unique role combines some responsibilities of an AI Solution Architect and an Educational Content Author, allowing you to develop skills and grow in different directions as a Solution Architect and/or Developer Advocate while working together with an amazing team of Nebius AI Engineers and Solution Architects.

Your responsibilities will include:

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 (universities, e.g., Stevens, MIT) to understand their requirements and develop solution architectures that align with their needs: design and document 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

We expect you to have:

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 IaC 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 PyTorch 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, DevOps, or a similar technical role

Demonstrated experience with cloud technologies and infrastructure

Previous work with infrastructure-as-code, containerization, and cloud environments

It will be an added bonus if you have

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