• Support networking technologies such as Spectrum-X and work with customers on their technical challenges and requirements during pre-sales activities • Develop proof-of-concept materials for innovative technologies for use by early adopters • Gain customers’ trust and understand their needs to help design and deploy cutting-edge NVIDIA networking platforms to run AI and HPC workloads • Address sophisticated and highly-visible customer issues • Work closely with R&D teams to develop new features for customers • Help with product requirements along side engineering and product marketing

• Set up, adjust, and verify AI factory environments across multi-GPU and multi-node Linux clusters. • Ensure configurations align with guidelines for NCCL, collectives, and distributed training frameworks. • Own the execution of key AI/LLM benchmarks, including setup, orchestration, result collection, and analysis. • Investigate and resolve issues when training jobs or benchmarks fail, hang, or underperform. • Build and improve observability for AI factories (metrics, logs, traces, dashboards) to understand workload behavior and system health. • Develop automation (Python, Shell) for running benchmarks, collecting results, and performing regression checks • Examine communication patterns and NCCL usage for AI/LLM workloads, concentrating on collectives such as AllReduce and AllToAll. • Recommend changes to job configuration, parallelism strategies, and cluster settings to improve throughput, latency, and scaling efficiency. • Work closely with hardware, software, networking, datacenter, and product teams to prepare AI factories for customer use. • Contribute to documentation, guidelines, and readiness collateral that support internal collaborators and customer-facing teams.

• NVIS Data Center deployment planning: Collaborate with product and engineering groups to understand NVIDIA’s reference architectures for data center infrastructure, encompassing power distribution, cooling systems, controls and monitoring, and network/cabling layout. • Assist customers and partners in quickly integrating this architecture into sophisticated and dependable data center projects. • Build and construction oversight: Review and appraise customers' and partners' infrastructure build plans, verifying their compliance with NVIDIA reference architecture, industry standards, and regulatory requirements. • Deliver mentorship, expertise, and suggestions to optimize performance, scalability, and cost-effectiveness. • Ensure collaboration with our customers and partners on reference architecture, guidelines, and processes to support their successful deployments. • Analyze the operational efficiency, reliability, and readiness of data center infrastructure components before launching AI/HPC clusters. • Prepare and complete comprehensive audit plans and conduct pre-deployment audits to detect potential issues, risks, and areas for improvement. • Work in close partnership with engineering, product, infrastructure, and customer-facing groups to guarantee consistency on reference architecture, deployment standards, and readiness criteria. • Quality Assurance: Implement and make quality assurance processes to ensure that deployments meet established specifications and performance benchmarks. • Conduct detailed bring-up, testing, and commissioning to validate the functionality and reliability of infrastructure components. • Continuous Improvement: Drive continuous improvement initiatives to improve reliability, resilience, and sustainability. • Find opportunities to streamline processes, automate repetitive tasks, and apply new technologies to optimize infrastructure operations. • Collaboration and Communication: Cooperate and exchange information across internal teams, external vendors, and customers to guarantee the smooth integration of data center infrastructure solutions. • Serve as a subject matter expert and main contact for infrastructure-related inquiries and urgent issues.

• Primary responsibilities will include deploying, managing, and validating AI/HPC infrastructure in Linux-based environments for first of kind NVIDIA hardware. • Be the domain authority with NVIDIA design engineering teams and external customers during planning calls through implementation. • Create and handover related documentation and perform knowledge transfers required to support customers as they roll out some of the most sophisticated systems in the world! • Provide feedback to internal teams such as opening bugs, documenting workarounds, and suggesting improvements.