Senior Solutions Architect, Cloud Infrastructure and DevOps

About This Role

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 the team building 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 multi-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 solving 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 partners.
• Support Research & 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 as the 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 (or equivalent experience), with 8+ years of professional experience in leading scalable cloud environments and automation engineering roles.
• Cloud & HPC Expertise: Shown understanding of networking fundamentals, data center architectures, and hands-on experience leading HPC/AI clusters, including deployment, optimization, and solving.
• NVIDIA GPU Expertise: Validated 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-accelerated and HPC environments.
• Hardware & Software Knowledge: Strong familiarity with HPC and AI technologies (CPUs, GPUs, high-speed interconnects) and supporting software stacks.
• Linux & Storage Systems: Deep knowledge of Linux (RedHat, Ubuntu), OS-level security, and protocols. Experience with storage solutions such as Lustre, GPFS, ZFS, XFS, and emerging Kubernetes storage technologies.
• Automation & Observability: Proficiency in Python and Bash scripting, configuration management, and Infrastructure-as-Code tools (e.g., Ansible, Terraform). Experience with observability stacks (Grafana, Loki, Prometheus) for monitoring, logging, and building fault-tolerant systems.
• Solution Architecture & Customer Engagement: Strong background in crafting scalable solutions and providing consultative support to customers, including leading architectural reviews and speaking publicly to executive partners.

Ways To Stand Out From The Crowd

• Knowledge of CI/CD pipelines for software deployment and automation.
• Experience working with NVIDIA GPU and Network Operators to manage automated resource lifecycle in Kubernetes environments.
• Solid hands-on knowledge of Kubernetes and container-based microservices architectures.
• Experience with NVIDIA GPU and Network Operator for automated GPU as well as network resources lifecycle management in Kubernetes environments.
• Experience with NVIDIA Base Command Manager (BCM) for provisioning, managing, and supervising GPU clusters at scale as well as background with RDMA-based fabrics (InfiniBand or RoCE) in HPC or AI environments.

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