AI/ML Specialist Solutions Architect
Nebius
Job Overview
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Job Description
About Nebius
Nebius is leading a new era in cloud computing to serve the global AI economy. We create the tools and resources our customers need to solve real-world challenges and transform industries, without massive infrastructure costs or the need to build large in-house AI/ML teams. Our employees work at the cutting edge of AI cloud infrastructure alongside some of the most experienced and innovative leaders and engineers in the field.
Where We Work
Headquartered in Amsterdam and listed on Nasdaq, Nebius has a global footprint with R&D hubs across Europe, North America, and Israel. The team of over 800 employees includes more than 400 highly skilled engineers with deep expertise across hardware and software engineering, as well as an in-house AI R&D team.
The Team
Customer experience at Nebius AI Cloud involves tackling customers’ challenges and directly impacting their success by solving real-world AI and ML problems at massive GPU cloud scale. You’ll not only resolve issues, but play a key role in shaping clients’ business success by optimizing their AI solutions. Working with advanced GPUs such as H200, B200 and GB200, as well as modern ML frameworks, you’ll influence the development of the Nebius AI Cloud and gain experience at the intersection of infrastructure and AI. With minimal bureaucracy, you’ll have the freedom to innovate, take ownership and drive change. Opportunities for growth are abundant in this vibrant and supportive professional community.
The Role: AI/ML Specialist Solutions Architect
We seek an experienced AI/ML Specialist Solutions Architect to support AI-focused customers leveraging Nebius services. In this role, you will be a trusted advisor, collaborating with clients to design scalable AI solutions, resolve technical challenges and manage large-scale AI deployments involving hundreds to thousands of GPUs.
You’re welcome to work on-site in Amsterdam or remotely from any other EU country.
Your Responsibilities
- Designing customer-centric solutions that maximize business value and align with strategic goals.
- Building and maintaining long-term relationships to foster trust and ensure customer satisfaction.
- Delivering technical presentations, producing whitepapers, creating manuals and hosting webinars for audiences with varying technical expertise.
- Collaborating with engineering and product teams to effectively prioritize and relay customer feedback.
What We Expect From You
- 3+ years of experience with cloud technologies in MLOps engineering, Machine Learning engineering or similar roles.
- Strong understanding of ML ecosystems, including models, use cases and tooling.
- Proven experience in setting up and optimizing distributed training pipelines across multi-node and multi-GPU environments.
- Hands-on knowledge of frameworks like PyTorch or JAX.
- Excellent verbal and written communication skills.
Bonus Points
- Expertise in deploying inference infrastructure for production workloads.
- Ability to transition ML pipelines from POC to scalable production systems.
Preferred Tooling
- Programming Languages – Python, Go, Java, C++
- Orchestration – Kubernetes (K8s), Slurm
- DevOps Tools – Git, Docker, Helm
- Infrastructure as Code (IaC) – Terraform
- ML Frameworks and Libraries – PyTorch, TensorFlow, JAX, HuggingFace, Scikit-learn
Key skills/competency
- AI
- Machine Learning
- MLOps
- Cloud Technologies
- Distributed Training
- PyTorch
- JAX
- Kubernetes
- Terraform
- Solution Architecture
How to Get Hired at Nebius
- Research Nebius's culture: Study their mission, values, recent news, and employee testimonials on LinkedIn and Glassdoor.
- Tailor your resume: Highlight MLOps, distributed training, cloud AI expertise, and solutions architecture experience for Nebius.
- Showcase technical depth: Emphasize hands-on experience with PyTorch, JAX, Kubernetes, and large-scale GPU environments.
- Prepare for solutions architecture: Demonstrate strong problem-solving abilities, customer-centric design, and technical communication skills.
- Highlight communication skills: Practice articulating complex AI concepts clearly to both technical and non-technical audiences.
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