About the company
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.
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.
Listed on Nasdaq (NBIS) and headquartered in Amsterdam, we have a global footprint with R&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&D.
Responsibilities
- Own end-to-end technical presales and solution delivery for a diverse portfolio of strategic customers including AI-native and digital-native companies, strategic and large enterprises, and government organizations, from initial discovery through PoC, architecture design, and production readiness.
- Serve as a trusted technical advisor to senior customer stakeholders (CTO, Head of ML, Platform Engineering, AI Infrastructure teams).
- Lead complex ML/AI infrastructure and MLOps architectures, including large-scale training and inference workloads on GPU-accelerated cloud platforms.
- Design, validate, and document reference architectures, Infrastructure-as-Code solutions, and best-practice deployment patterns using Nebius AI.
- Drive and execute advanced PoCs, workshops, architecture reviews, and executive-level presentations to demonstrate value and accelerate customer adoption.
- Partner closely with Sales, Product, Engineering, and Support to represent real-world customer requirements and influence product roadmap decisions.
- Act as a single point of technical authority for key customer scenarios across internal teams (product, support, marketing).
- Support strategic marketing initiatives including conferences, hackathons, webinars, customer case studies, and technical thought leadership.
- Mentor and provide technical leadership to other Solutions Architects, helping raise the overall technical bar of the organization.
Requirements
- 10+ years of experience in senior technical roles such as Solutions Architect, Systems Architect, ML Platform Engineer, or similar — with significant customer-facing responsibilities.
- Proven track record of end-to-end delivery of complex presales engagements, including discovery, solution design, PoCs, and production transition.
- Deep hands-on experience with large-scale ML-based workloads, including GPU training and inference at scale.
- Strong expertise in cloud infrastructure and MLOps, including Kubernetes-based platforms and distributed systems.
- Solid hands-on experience with Infrastructure as Code and configuration management (Terraform, Ansible), and strong Python skills.
- Deep understanding of GPU computing stacks for ML/AI workloads (drivers, CUDA, libraries, performance optimization).
- Exceptional communication skills — able to clearly articulate complex technical concepts to both technical and executive audiences.
- A highly customer-centric mindset with the ability to balance customer needs, technical feasibility, and product strategy.
- Fluency in Korean and English, with the ability to communicate effectively with customers and stakeholders in both languages.
Nice to have:
- Prior experience as a Senior AI/ML Specialist Solutions Architect, Technical ML Product Manager, or in a similar senior AI/ML-focused role.
- Hands-on experience with HPC and ML orchestration frameworks (e.g., Slurm, Kubeflow).
- Practical experience with deep learning frameworks such as PyTorch and TensorFlow.
- Strong understanding of the cloud ML ecosystem across major providers (NVIDIA, AWS, Azure, Google Cloud).
- Experience influencing product direction based on customer feedback and real-world usage patterns.
- Experience leading and contributing to end-to-end large scale ML deployment projects on all layers of the stack.
Conditions
- Competitive compensation
- Career growth and learning opportunities
- Flexibility and ownership
- Collaborative and innovative culture
- Opportunity to work on impactful AI projects
- International environment and talented teams