2 months ago
Gurgaon, IndiaSenior
Responsibilities
- Build AI/HPC infrastructure for new and existing customers.
- Support reliability and operational aspects of large-scale AI clusters, including performance at scale, monitoring, logging, and alerting.
- Improve service lifecycles from inception and design through deployment, operation, and refinement.
- Develop infrastructure automation, operational monitoring and alerting, and self-service resource tooling.
- Deploy monitoring solutions for servers, networks, and storage.
- Troubleshoot infrastructure from bare metal and operating systems through software stacks and applications.
- Develop and document standard methodologies for customers and internal teams and participate in POCs/POVs.
- Engage with customers, partners, and internal teams on large-scale networking projects.
Requirements
- BS, MS, PhD, or equivalent experience in Computer Science, Data Science, Electrical/Computer Engineering, Physics, Mathematics, or another engineering field.
- At least 5 years of work or research experience in networking fundamentals, the TCP/IP stack, and data center compute architecture.
- Advanced knowledge of HPC, AI, EVPN, BGP, OSPF, and VXLAN protocols.
- Deep understanding of data center architecture fundamentals, including compute, storage, PFS, InfiniBand, Ethernet, and NVLink.
- Experience with HPC performance benchmarks, cluster health checks, and profiling tools.
- Python programming, Bash scripting, and advanced Linux experience.
- Extensive experience delivering automated network provisioning and using Jenkins, Ansible, Puppet, Chef, or similar automation and configuration management tools.
- Working knowledge of Ethernet, InfiniBand, and RDMA principles.
- Strong customer-facing communication and collaboration skills, including English listening skills.
- Willingness to travel.
- Preferred qualifications include knowledge of CPU or GPU architecture, Kubernetes, container-related microservice technologies, RDMA fabrics, Linux or networking certifications such as CCNP or CCIE, NVIDIA-related certifications, and observability stack implementation experience.
Tech Stack
Categories
Solutions Engineering
About Nvidia
Nvidia designs and sells GPUs and accelerated computing platforms for data centers, AI/ML, graphics, gaming, and automotive, monetizing through hardware, software platforms (CUDA, AI frameworks), and systems like DGX and networking. Customers include cloud providers, enterprises, researchers, and OEMs. Founded in 1993 and headquartered in Santa Clara, it is a public company traded on NASDAQ under NVDA.