Nvidia Solutions Architect CSP Networking 

United States, Texas 

729557780

What you’ll be doing:

• A huge part of the day-to-day job is driving adoption for end-to-end data workflows and networking solutions demonstrating the NVIDIA AI Enterprise (NVAIE) software offering. You will address customer problems by building systems using the newest tools in software defined networking, cyber security, DevOps, orchestration, Kubernetes, and other workflow tools.

• Document and guide others what you know and have learned through delivering demos and developing Prof Of Concepts. This can vary from building hands-on training, to writing papers, developer blogs.

• We make heavy use of conferencing tools, travel may be required for this role, as we work together with the customer to scope and deliver projects.

• You will be the person that helps bring NVIDIA technology to life in the Enterprise. You are empowered and given all the tools to achieve this with the backing of all of NVIDIA, other Solution Architects and our Engineering teams. You’ll get to be the face and brains of NVIDIA that our customers will rely on.

What we need to see:

• BS or MS degree in Engineering, Computer Science, Data Science, or equivalent experience.

• 3+ years of meaningful working experience

• Ideal candidates will have an established track record working with DevOps, virtualization and HPC cluster management tools; including Docker/Containers, Kubernetes, Ansible, VMware/TKG.

• Expertise with dense datacenter design including compute, storage, and networking. Familiarity with Cloud and hybrid cloud architectures.

• Experience with L2-L7 networking protocol stack.

• Strong analytical and problem-solving skills.

• Ability to multitask efficiently in a dynamic environment.

• Clear written and oral communications skills with the ability to effectively collaborate with executives and engineering teams.

• Successful candidates will be able to demonstrate a strong desire to share knowledge with clients, partners, and co-workers.

Ways to stand out from the crowd:

• Strong systems engineering, coding, and debugging skills. Including experience with Python, Ansible, Go, C/C++, Bash, Linux and Windows.

• Demonstrate expertise through developed projects or Open Source contributions in DPDK, IP Sec, HPC, Cloud Native Projects, Kubernetes, Slurm for enabling GPU workloads.

• Experience in supporting Deep Learning, Machine Learning or HPC networking infrastructures; experience with networking technologies as well as understanding deep learning frameworks such as TensorFlow or PyTorch.

• Show the ability to dig into unfamiliar territories to tackle complex problems through examples in previous work.