Solution Architect - AI Infrastructure

Ready to power the next wave of generative AI and machine learning that’s reshaping industries worldwide?

Join a pioneering AI infrastructure innovator delivering full-stack cloud services and large-scale GPU platforms that accelerate research, development, and deployment for startups, enterprises, and research institutions across the globe. Their AI-native cloud combines hyperscale performance with supercomputer-level computing power to support the entire machine learning lifecycle. This is a team of expert engineers and AI researchers driven by curiosity, innovation, and a mission to transform how the world builds and uses artificial intelligence.

Step into a role where your work drives breakthrough AI solutions, fuels real-world impact, and places you at the forefront of next-generation cloud and AI technology!

• Act as a trusted technical advisor to customers, providing architecture guidance and best practices throughout the engagement lifecycle.
• Lead technical PoCs, workshops, presentations, and training sessions focused on GPU cloud infrastructure and ML workloads.
• Collaborate with customers to understand business and technical requirements, translating them into scalable cloud and MLOps architectures.
• Design and document infrastructure-as-code solutions, technical documentation, and implementation guides in collaboration with support and documentation teams.
• Help customers optimise ML pipelines for performance, scalability, and cost efficiency across GPU-powered cloud environments.
• Serve as the internal point of reference for customer use cases, feeding insights to product, engineering, support, and marketing teams.
• Support go-to-market and community efforts through participation in events such as hackathons, conferences, workshops, and webinars.

• 5–10+ years of experience in a technical role such as Cloud Solutions Architect, Systems/Network Engineer, Platform Engineer, or Developer, with strong cloud exposure.
• Hands‑on experience with Infrastructure as Code and configuration management tools (Terraform and/or Ansible).
• Strong experience with Kubernetes and production Linux environments.
• Ability to write automation or tooling in Python.
• Solid understanding of GPU computing for ML training and inference, including GPU software stacks (drivers, CUDA, related libraries).
• Excellent communication skills and a strong customer‑centric mindset.

• Equity (RSUs)

• $315,000 OTE