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LeadFull-timePythonRustAWSAzureKubernetesTerraformTensorFlowPyTorchAIML
Salary Range
$53,550 - $72,450 /year
This salary is estimated based on similar roles. The actual salary may vary.
About 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.
Key Responsibilities
- →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.
- →Support strategic marketing initiatives including conferences, hackathons, webinars, customer case studies, and technical thought leadership.
- →Mentor and provide technical leadership to other Solutions Architects.
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.
- ✓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.
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