NVIDIA is building the world’s leading AI company, and we are looking for a Solutions Architect skilled in full cycle Gen AI development and deployment to assist customers with adoption of NVIDIA's full-stack technologies as well as building and deploying solutions around Generative AI and other related GPU-accelerated technologies in which intelligent agents can learn, reason, and interact with the world. As part of the Hospitality and Travel Solutions Architecture team, we work with some of key hotel and transportation chains to integrate accelerated computing and AI into their stacks. A Solutions Architect is the first line of technical expertise between NVIDIA and our Auto customers so you will engage directly with developers, researchers, and data scientists with some the most strategic customers as well as work directly with business and engineering teams on product strategy as it pertains to these customers. Join us in this exciting endeavor!
What You Will Be Doing:
Provide hands-on technical mentorship to partners and customers on Nvidia GenAI stack. Guide customers to develope and deploy Agentic AI workflows on our platforms, quantifying the benefits of our accelerated computing software and hardware.
Build demonstrations and POCs for solutions that address critical business needs of our customers. Help in draft requirements for missing features to unblock progress at customers/partners.
Educate customers on new NVIDIA GenAI technologies and platforms. Prepare and deliver technical training presentations and workshops.
Build collateral (notebook/ blog) applied to the industry use-cases.
Partner with NVIDIA engineering, product, sales teams to secure design wins at customers. Enable development and growth of NVIDIA product features through customer feedback and POC evaluations.
What We Need To See:
Master's or Ph.D. in Computer Science, Artificial Intelligence, or equivalent experience.
8+ years of hands-on experience in a technical AI role, with a strong emphasis on Gen AI.
Proficient in the latest and greatest model architectures and be able to articulate the computational complexities of each.
Proven track record of deploying and optimizing LLM models for inference in production environments using well known inferencing engines e.g., vLLM, TRT-LLM, SGLang, etc.
Expertise in training and fine-tuning LLMs using popular frameworks such as TensorFlow, PyTorch, or Hugging Face Transformers.
Solid understanding of GPU cluster architecture and the ability to bring to bear parallel processing for accelerated model training and inference.
Experience with basic DevOps tools e.g., Docker, Kubernetes, GitLab, Linux Command Line, Shell, etc.
Excellent communication and teamwork skills with the ability to explain complex technical concepts to both technical and non-technical collaborators.
Experience leading workshops, training sessions, and presenting technical solutions to diverse audiences.
Ways To Stand Out From The Crowd:
Experience with Agentic AI frameworks, tools, and protocols e.g., LangChain, LangGraph, MCP.
Understand Multimodal LLMs, VLMs, etc.
Experience in deploying LLM models at scale on mainstream cloud providers (e.g., AWS, Azure, GCP).
Proven track record to profile and optimize inference latency and throughput, memory and I/O utilization.
Mathematical understanding of different parallelisation techniques in Gen AI
You will also be eligible for equity and benefits.
This posting is for an existing vacancy.
NVIDIA uses AI tools in its recruiting processes.
NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.Skills Required
- Master's or Ph.D. in Computer Science, Artificial Intelligence, or equivalent experience
- 8+ years of hands-on experience in a technical AI role with strong emphasis on Generative AI
- Proficiency in modern model architectures and their computational complexities
- Experience deploying and optimizing LLM inference in production using engines such as vLLM, TensorRT-LLM, or SGLang
- Expertise training and fine-tuning LLMs with TensorFlow, PyTorch, or Hugging Face Transformers
- Understanding of GPU cluster architecture and parallel processing for model training and inference
- Experience with DevOps tools including Docker, Kubernetes, GitLab, Linux command line, and Shell
- Excellent communication and teamwork skills, including explaining complex technical concepts to technical and non-technical audiences
- Experience leading workshops, training sessions, and technical presentations
- Experience with agentic AI frameworks, tools, or protocols such as LangChain, LangGraph, or MCP
- Understanding of multimodal LLMs and vision-language models
- Experience deploying LLMs at scale on AWS, Azure, or GCP
- Experience profiling and optimizing inference latency, throughput, memory, and I/O utilization
- Mathematical understanding of parallelization techniques in Generative AI
NVIDIA Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about NVIDIA and has not been reviewed or approved by NVIDIA.
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Equity Value & Accessibility — Feedback suggests equity grants, stock options/RSUs, and a discounted ESPP materially boost total compensation and can deliver significant wealth-building when the stock performs well.
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Healthcare Strength — Feedback suggests medical, dental, and vision plans with in-network preventive care at 100%, telehealth, and concierge-style support are comprehensive and valued.
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Parental & Family Support — Feedback suggests up to 22 weeks fully paid parental leave, fertility coverage, and adoption/surrogacy reimbursement provide strong family-focused benefits.
NVIDIA Insights
What We Do
NVIDIA’s invention of the GPU in 1999 sparked the growth of the PC gaming market, redefined modern computer graphics, and revolutionized parallel computing. More recently, GPU deep learning ignited modern AI — the next era of computing — with the GPU acting as the brain of computers, robots, and self-driving cars that can perceive and understand the world. Today, NVIDIA is increasingly known as “the AI computing company.”
