Senior Solutions Architect, Cloud Infrastructure and DevOps

We are looking for a Senior Cloud Infrastructure and DevOps Solutions Architect to join our NVIDIA Infrastructure Specialist Team. As a Senior Solutions Architect, you will engage directly with customers, partners, and multi-functional teams to assess, architect, and guide the implementation of large-scale infrastructure projects.

The scope of this role spans system architecture, Kubernetes-based platforms, and automation,serving as both a trusted advisor and a hands-on technical leader. You will advise on and help maintain large-scale computational and AI infrastructure, including monitoring, logging, and workload orchestration (Kubernetes and Linux job schedulers).

Key responsibilities include:

• Providing consultative guidance and performing hands-on solving across the full stack,from bare metal and operating system, through the software stack, container platform, networking, and storage.
• Assessing customer environments and recommending optimized, production-ready Kubernetes-based container platforms integrated with enterprise-grade networking and storage solutions.
• Serving as a key technical resource: developing, refining, and documenting standard methodologies and operational guidelines to be shared with internal teams and customer partners.
• Supporting Research & Development activities and engaging in POCs/POVs to validate new features, architectures, and upgrade approaches.
• Creating and delivering high-quality documentation, including runbooks, onboarding materials, and best-practice guides for customers and internal teams.
• Acting as the technical leader for assigned customer accounts, providing strategic guidance on DevOps and platform architecture and influencing long-term infrastructure and operations decisions.

Requirements:

• 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.

Preferred qualifications include:

• 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.