Solutions Architect, DevOps

NVIDIA is looking for a Senior Cloud Infrastructure and DevOps Solutions Architect to join its NVIDIA Infrastructure Specialist Team. Academic and commercial organizations around the world are using NVIDIA products to redefine deep learning and data analytics, and to power next-generation data centers. Join theteambuilding and advising on many of the largest and fastest AI/HPC systems in the world!

We are looking for someone who combines deep technical expertise with strong consulting and communication skills. This role will engage directly with customers, partners, and cross-functional teams to assess, architect, and guide the implementation of large-scale infrastructure projects. The scope spans system architecture, Kubernetes-based platforms, and automation—serving as both a trusted advisor and a hands-on technical leader.

What You’ll Be Doing:

Advise on and help maintain large-scale computational and AI infrastructure, including monitoring, logging, and workload orchestration (Kubernetes and Linux job schedulers).

Provide consultative guidance and perform hands-on troubleshooting across the full stack—from bare metal and operating system, through the software stack, container platform, networking, and storage.

Assess customer environments and recommend optimized, production-ready Kubernetes-based container platforms integrated with enterprise-grade networking and storage solutions.

Serve as a key technical resource: develop, refine, and document standard methodologies and operational guidelines to be shared with internal teams and customer stakeholders.

Support Development activities and engage in POCs/POVs to validate new features, architectures, and upgrade approaches.

Create and deliver high-quality documentation, including runbooks, onboarding materials, and best-practice guides for customers and internal teams.

Act asthe technical leader for assigned customer accounts, providing strategic guidance on DevOps and platform architecture and influencing long-term infrastructure and operations decisions.

What We Need to See:

Education & Experience: BS/MS/PhD in Computer Science, Electrical/Computer Engineering, Physics, Mathematics, or related fields, with 5+ years of professional experience in managing scalable cloud environments and automation engineering roles.

Cloud & HPC Expertise: Proven understanding of networking fundamentals,data center architectures, and hands-on experience managing HPC/AI clusters, including deployment, optimization and troubleshooting.

NVIDIA GPU Expertise: Demonstrated hands-on experience deploying, configuring, and optimizing NVIDIA GPU-accelerated infrastructure, including driver management, CUDA toolkit integration, and GPU workload profiling.

Kubernetes & AI/ML Workloads: Extensive experience with Kubernetes for container orchestration, resource scheduling, scaling, and integration with GPU-