Senior Solutions Architect - Cloud Infrastructure

Senior Solutions Architect - Cloud Infrastructure at NVIDIA

NVIDIA is pleased to announce an opening for a Senior Solutions Architect - Cloud Infrastructure. We are looking for a passionate expert in cloud infrastructure engineering. If you enjoy leading projects that advance cloud-based AI and resilience in large environments, please continue reading. NVIDIA is recognized as a top employer in technology and offers competitive benefits. We host some of the most innovative and forward-thinking talent worldwide. If you are creative, self-driven, and ready to apply your skills in a fast-paced setting, we encourage you to apply.

What You'll Be Doing

• Serving as a recognized technical expert and trusted advisor on NVIDIA's GPU-accelerated cloud offerings and high-performance networking solutions.
• Helping clients build resilient cloud infrastructures that collect and use system data, optimized for AI Factory deployments.
• Architecting and validating high-performance interconnect solutions by using accelerated networking technologies like InfiniBand, RoCE (RDMA over Converged Ethernet), and GPUDirect. These solutions enable efficient large-scale AI training and inference workloads.
• Leading several complex projects and working closely with engineering groups to achieve build wins, tackle issues, and launch solutions into production. The focus is on developing strong tools for observability and failure recovery.
• Working closely with Sales Account Managers to lead customer proof-of-concept evaluations for Microsoft/Azure-focused opportunities.
• Demonstrating leadership through project ownership—defining projects of varying scope and complexity, coordinating experiments, tests, and evaluations that solve customer challenges.
• Developing research collaboration programs with key customers and partners.
• Serving as an internal reference for datacenter, large-scale computing, and networking solutions within the NVIDIA technical community. Mentoring less experienced team members and promoting collaboration across departments.

What We Need To See

• 12+ years of experience in cloud infrastructure engineering, AI/ML systems, or extensive distributed systems (may be less with highly relevant industry experience).
• A BS in Computer Science, Electrical Engineering, Mathematics, or Physics, or equivalent experience.
• Recognized expertise in cloud computing and large-scale computing systems, with a strong understanding of high-performance networking architectures including InfiniBand, RDMA, RoCE, or similar low-latency interconnect technologies critical for AI/HPC workloads.
• Proficiency in Linux, Windows Subsystem for Linux, and Windows is required.
• A passion for machine learning and AI is essential. You should have the drive to continually learn and apply new technologies.
• Excellent interpersonal skills, including the ability to explain complex technical topics to non-experts and influence collaborators at the executive level.
• Track record of successfully managing multiple engagements during implementation of new technology and products into complex projects.

Ways To Stand Out From The Crowd

• Extensive knowledge of Microsoft Azure, especially its GPU-accelerated and HPC services.
• Skilled in deploying and managing cloud-native solutions on leading cloud platforms, concentrating on GPU-accelerated workloads.
• Expertise in crafting and optimizing AI Factory architectures, including network fabric design, GPUDirect RDMA, NCCL tuning, and multi-node training performance optimization.
• Expertise with orchestration tools like Slurm and Kubernetes, along with familiarity with NVIDIA's DGX Cloud, Base Command Platform, and its ecosystem.
• Hands-on experience crafting telemetry systems and failure recovery mechanisms for large-scale cloud infrastructures, including observability tools such as Grafana, Prometheus, and OpenTelemetry or equivalent experience.
• Contributions to open-source projects demonstrating proficiency in cloud-AI/infrastructure engineering, with a recognized standing as a leader in cloud infrastructure or AI/ML fields.

Key skills/competency

• Cloud Infrastructure Engineering
• AI/ML Systems
• High-Performance Networking
• GPU Acceleration
• InfiniBand/RDMA
• Microsoft Azure
• Kubernetes/Slurm
• Observability Tools (Grafana, Prometheus)
• Distributed Systems
• Solution Architecture