Cloud Solutions Architect

3 weeks ago

Canada Nebius Group Full time
About Nebius AI

Nebius AI is a cutting-edge platform that provides high-end infrastructure and tools for training, fine-tuning, and inference. With a global footprint and a team of over 500 skilled engineers, we aim to become the leading AI cloud for AI practitioners worldwide.

Our platform is built around the talents of our engineers, who have a proven track record in developing sophisticated cloud and ML solutions. This allows us to offer a true hyperscaler-cloud experience tailored for AI practitioners.

We are expanding our infrastructure and plan to add new colocation data centers in Europe and North America. Our Finnish data center is home to ISEG, the most powerful commercially available supercomputer in Europe.

The Role

We are seeking a highly skilled and customer-focused professional to join our team as a Solutions Architect specializing in Cloud and MLOps. As a 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.

You will act as a trusted advisor to our clients, providing technical expertise and guidance throughout the engagement. You will collaborate with clients to understand their business requirements and develop solution architecture that aligns with their needs.

Your Responsibilities
  • Act as a trusted advisor to our clients, providing technical expertise and guidance throughout the engagement.
  • Collaborate with clients to understand their business requirements and develop solution architecture that aligns with their needs.
  • Help customers to optimize pipeline performance and scalability to ensure efficient utilization of cloud resources and services powered by Nebius AI.
We Expect You to Have
  • 5+ 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, and skills in writing code in Python.
  • Solid understanding of GPU computing practices for ML training and inference workloads, GPU software stack components, including drivers and libraries (e.g. CUDA, OpenCL).
It Will Be an Added Bonus If You Have
  • Hands-on experience with HPC/ML orchestration frameworks (e.g. Slurm, Kubeflow).
  • Hands-on experience with deep learning frameworks (e.g. TensorFlow, PyTorch).
  • Solid understanding of cloud ML tools landscape from industry leaders (NVIDIA, AWS, Azure, Google).

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