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This listing was closed on Aug 13, 2026.
Browse Open JobsAI/ML Specialist Solutions Architect
Summary by AIEngineer.careers
AI/ML Specialist Solutions Architect is a AI Solutions Architect role (full-time). with Nebius. in REMOTE, WORLDWIDE. Compensation shown: $81K–$259K (est.). Imported listing (source: jobs-radar.com). Apply on the employer's site (jobs-radar.com).
Imported listing (source: jobs-radar.com) · Apply on jobs-radar.com
REMOTE · WORLDWIDE
$81K–$259K (est.)
Full-time
SENIOR
The sections below reproduce the third-party job description for reference. AIEngineer.careers does not write or control this text.
Imported job description
Sourced from jobs-radar.com
Nebius seeks an AI/ML Specialist Solutions Architect to design and implement cloud solutions for AI/ML workloads. This role involves advising clients, conducting proof of concepts, and optimizing GPU cloud infrastructure. The position is fully remote, targeting candidates in the US or Canada.
Responsibilities
- Act as a trusted advisor to clients, providing technical expertise and guidance.
- Conduct PoC, workshops, presentations, and training sessions on GPU cloud technologies.
- Collaborate with clients to design and document Infrastructure as Code solutions.
- Help customers optimize pipeline performance and scalability using Nebius AI Cloud.
- Act as a single point of expertise for customer scenarios across product, support, and marketing teams.
Requirements
- 5-10+ years of experience as a cloud solutions architect, system/network engineer, or similar role with cloud computing focus.
- Strong hands-on experience with IaC and configuration management tools (Terraform/Ansible), Kubernetes, and Python.
- Solid understanding of GPU computing for ML training and inference, including CUDA and OpenCL.
- Excellent communication skills and customer-centric mindset.
Nebius
Amsterdam · NL · 1590+ employees
Nebius is an AI cloud company providing full-stack infrastructure for AI developers and enterprises. Its platform supports the complete AI journey, including data storage, model training, tuning, and production runtime deployment.
