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. #J-18808-Ljbffr Career Techniques