Solutions Architect

The Solutions Architect will serve strategic HPC and cloud customers. You will be a trusted technical advisor, helping clients design, deploy, and scale compute-intensive workloads — from large-scale AI/ML training and inference to advanced scientific simulations and data-driven research.

Responsibilities

Customer Advisory

• Serve as the primary technical point of contact for key and strategic customers, troubleshooting complex HPC, cloud, and AI/ML challenges.
• Guide customers in optimizing compute performance for ML training, inference, and simulation workloads, ensuring seamless integration and scalability on the platform.

Sales & Growth

• Partner with the sales team to identify growth opportunities, promote new platform capabilities, and deliver compelling technical presentations and demos.

Product Collaboration

• Act as a bridge between customers and the product team — relaying feedback, surfacing feature requests, and ensuring the roadmap reflects real customer needs.

Stakeholder Engagement

• Engage with internal and external stakeholders to negotiate solutions, drive alignment, and remove blockers for strategic accounts,

Documentation

• Contribute to the development of technical documentation, reference architectures, and best practice guides for the ecosystem.

Requirements

Essential

• 5–10+ years in roles such as Cloud Solutions Architect, Technical Account Manager, or Customer Engineer, with hands-on experience in cloud and HPC environments.
• Proficiency with Infrastructure as Code tools such as Terraform and Ansible.
• Experience with Kubernetes and Python programming.
• Solid understanding of GPU and HPC computing, including ML training, inference workloads, and relevant stacks (e.g. CUDA, OpenCL, MPI).
• A customer-first mindset with a proven ability to build trust and foster long-term technical relationships.
• The ability to explain complex technical concepts clearly to both technical and non-technical audiences.

Bonus Points for

• Hands-on experience with HPC and ML orchestration frameworks (e.g. Slurm, Kubeflow, MPI).
• Experience with deep learning frameworks such as PyTorch or TensorFlow.
• Familiarity with tools and services from major cloud providers (AWS, Azure, GCP) and hardware vendors like NVIDIA.
• Strong project management skills with the ability to prioritize and deliver across multiple accounts.
• Experience mentoring technical teams and contributing to team growth.
• A track record of stakeholder negotiation and cross-functional collaboration.