Principal Solutions Architect APJ
Nebius · Singapore · 2026-07-27
About this role
About Nebius:
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.
The role
We are seeking an experienced, customer-obsessed Principal Solutions Architect to lead technical engagement with Nebius’ most strategic customers across multiple industry segments. This is a senior, high-impact role focused on complex presales, large-scale ML workloads, and end-to-end customer solution ownership — from early discovery through production adoption.
As a Principal Solutions Architect, you will act as a trusted technical partner for executive stakeholders, AI/ML leaders, and platform teams. You will shape solution strategy, drive complex PoCs, influence product direction, and ensure successful adoption of Nebius AI cloud for mission-critical ML/AI workloads.
You’re welcome to work remotely from Singapore.
Your responsibilities will include:
• Own end-to-end technical presales and solution delivery for strategic and enterprise customers, 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.
We expect you to have:
• 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.
Skills asked for
- r
- kubernetes
- terraform
- ansible
- python
- deep learning
- pytorch
- tensorflow
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