AI/ML Specialist Solutions Architect

<div class="content-intro"><p><strong>About Nebius:</strong></p> <p>Nebius is leading a new era in cloud infrastructure for the global AI economy. We are building a full-stack AI cloud platform that supports developers and enterprises from data and model training through to production deployment, without the cost and complexity of building large in-house AI/ML infrastructure.</p> <p>Built by engineers, for engineers. From large-scale GPU orchestration to inference optimization, we own the hard problems across compute, storage, networking and applied AI.</p> <p>Listed on Nasdaq (NBIS) and headquartered in Amsterdam, we have a global footprint with R&amp;D hubs across Europe, the UK, North America and Israel. Our team of 1,500+ includes hundreds of engineers with deep expertise across hardware, software and AI R&amp;D.</p></div><h3><strong>The role</strong></h3> <p>We seek an experienced 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.</p> <p>You’re welcome to work on-site in Amsterdam or remotely from any other EU country.</p> <p><strong>Your responsibilities will include:</strong></p> <ul> <li>Designing customer-centric solutions that maximize business value and align with strategic goals.</li> <li>Building and maintaining long-term relationships to foster trust and ensure customer satisfaction.</li> <li>Delivering technical presentations, producing whitepapers, creating manuals and hosting webinars for audiences with varying technical expertise.</li> <li>Collaborating with engineering and product teams to effectively prioritize and relay customer feedback.</li> </ul> <p><strong>We expect you to have:</strong></p> <ul> <li>3+ years of experience with cloud technologies in MLOps engineering, Machine Learning engineering or similar roles.</li> <li>Strong understanding of ML ecosystems, including models, use cases and tooling.</li> <li>Proven experience in setting up and optimizing distributed training pipelines across multi-node and multi-GPU environments.</li> <li>Hands-on knowledge of frameworks like PyTorch or JAX.</li> <li>Excellent verbal and written communication skills.</li> </ul> <p><strong data-stringify-type="bold">It will be an added bonus if you have:</strong></p> <ul> <li>Expertise in deploying inference infrastructure for production workloads.</li> <li>Ability to transition ML pipelines from POC to scalable producti