NVIDIA Solutions Architect (AI infrastructure)
NVIDIA Solutions Architect roles in AI infrastructure (AI factory, generative AI infrastructure, networking, supercomputing), and infrastructure solutions architect roles at clouds and server makers.
5 to 7+
years of experience asked
6
sizing and debugging cases
2
customer rounds
From NVIDIA job posts read on Nov 3, 2026.
Prepare for a company
Cases
Every question is open. The worked answers are in Pro, except the free ones.
10questions
Readiness check: 10 questions
30 daysto pretrain 34B
A training cluster for a pharma research customer
6,000agents on calls
Live suggestions for a telecom contact center
16nodes, as fast as 12
Four new nodes and no speed-up
512GPUs, 5 workloads
One GPU cluster, five kinds of work
1,536GPUs, then 4,096
The compute fabric for a regional GPU cloud
13spectral bands
GPUs at 33% on a satellite imagery job
15 monthsto go live
An AI forecasting platform for a weather agency
$2.5Mbudget
"Eight nodes for AI dubbing": a co-sell call
2.4Mpages a day
Take-home: an AI platform for a bank
What the job posts ask for
- Linux, distributed computing, GPU architectures and large AI clusters.
- GPU networks: InfiniBand, RoCE and GPUDirect RDMA, and debugging NCCL.
- Profiling AI workloads across compute, network, storage and orchestration.
- Kubernetes and Slurm, containers and monitoring. The GPU Operator and Network Operator help you stand out.
- Python and shell for automation, benchmarking and troubleshooting.
What the companies say about their interviews
NVIDIA
No published interview process that we found. Candidates report technical screens, a deep dive on systems they built, a design case, a troubleshooting scenario and a customer conversation.
Skills and labs
Each case names the skills it tests. These are the labs that train them, by skill.
Inference serving and optimization
6 labs
Inference serving and optimization
6 labs
GPU performance and CUDA
5 labs
GPU performance and CUDA
5 labs
Kubernetes and GPU cluster operations
14 labs
Kubernetes and GPU cluster operations
14 labs
- Welcome to NCA-AIIO Labs
- NVIDIA GPU Operator on k3s
- Inside the NVIDIA GPU Operator
- Kubernetes Resource Requests & Limits
- Multi-GPU-Type Targeting
- PriorityClass & Preemption
- Workload Controllers
- Persistent Storage for AI Workloads
- GPU Container Lifecycle
- Rolling Updates, Rollback & Blue-Green for AI Inference
- Stuck-Pending Triage Day
- GPU Observability
- GPU Health Checks + Auto-Remediation
- Incident Drill
AI networking and data center fabrics
No labs yet
AI networking and data center fabrics
No labs yet
No labs yet. The cases teach it: fabrics, NCCL and how to debug them.
Deep learning, training and fine-tuning
9 labs
Deep learning, training and fine-tuning
9 labs
Serving at scale: routing, capacity, caching, cost
4 labs
Serving at scale: routing, capacity, caching, cost
4 labs
Customer-facing skills
No labs yet
Customer-facing skills
No labs yet
Practised in the role-plays: discovery, proofs of concept, architecture docs, executives.
The behavioral round
No labs yet
The behavioral round
No labs yet
Practised in the follow-ups: your stories, told with numbers.
Certification prep in the same plan
Optional. Practice exams for certifications close to this role.
A source tag says which company and role reported a format, how many independent sources describe it and when. It doesn't mean a company uses our question. Every scenario and number is ours. Preporato is not affiliated with NVIDIA, CoreWeave, Microsoft. These cases help you prepare and don't promise an interview or an offer.