About the job
NVIDIA is seeking outstanding AI Solutions Architects to assist and support customers that are building solutions with our newest AI and accelerated computing technologies. At NVIDIA, our solutions architects work across product, engineering, sales, developer relations, business development, and partner teams to help customers design, deploy and optimize AI infrastructure.
This role will focus on helping ISVs adopt NVIDIA accelerated infrastructure for training, fine-tuning, inference, retrieval, and agentic AI workloads. This role is an excellent opportunity to work in an interdisciplinary team at NVIDIA! You will serve as a technical advisor for accelerated systems architecture, GPU and networking systems, cluster design, architectures, orchestration, validation, and production deployment for AI data centers.
Responsibilities
Partner with ISVs on discovery, architecture reviews, technical deep dives, POCs, benchmarks, demos, and production deployment guidance
Advise on the design, build-out, and optimization of accelerated AI infrastructure, including large-scale clusters
Support infrastructure design across compute, networking, storage, containers, observability, security, power, and data center operations
Drive adoption of systems monitoring, telemetry, and management tools to improve cluster utilization, reliability, performance and workload insight
Build repeatable reference architectures, deployment guides, sizing guidance, benchmark reports, technical playbooks, demos and whitepapers
Qualifications
Minimum
BS, MS, or PhD in Computer Science, Electrical/Computer Engineering, Physics, Mathematics, other Engineering or related fields (or equivalent experience)
8+ years of hands-on experience in AI infrastructure, accelerated computing, distributed systems, cloud infrastructure, high-performance computing, or machine learning platforms
Strong experience designing, deploying, and operating accelerated computing infrastructure at scale
In-depth knowledge of AI cluster orchestration, scheduling, automation and CI/CD deployment pipelines
Understanding of data center networking technologies such as InfiniBand, Ethernet, RDMA, network configuration or performance tuning
Familiarity with infrastructure requirements for AI workloads, including distributed training, inference serving, model deployment, storage performance, and cluster reliability
Excellent presentation, communication, problem-solving, documentation, and collaboration skills
Preferred
Experience architecting AI factories, large GPU clusters, multi-node training environments, production inference platforms
Experience deploying LLM training, fine-tuning, RAG, and inference workflows on large-scale AI infrastructure
Experience evaluating cluster performance using benchmarks such as MLPerf, HPL, or workload-specific performance tests
Applications and systems-level knowledge of OpenMPI, NCCL, distributed training frameworks, and GPU communication patterns
Experience delivering technical training, workshops, whitepapers, blogs, or mentoring engineers, researchers, and customers on AI/HPC infrastructure