Senior Solutions Architect, Gen AI

🕒 June 2

🏄 California – Remote

🏄 California – Remote

💵 $184k - $287.5k / year

⏰ Full Time

🟠 Senior

💻 Solutions Engineer

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NVIDIA

WebsiteLinkedInAll Job Openings

10,000+ employees

Founded 1993

🤖 Artificial Intelligence

🎮 Gaming

Artificial Intelligence • Gaming • Automotive

NVIDIA is a leading technology company specializing in accelerated computing and artificial intelligence. NVIDIA pioneers advancements in graphical processing units (GPUs), cloud computing, data centers, and virtual reality, with a focus on gaming, automotive, healthcare, and robotics industries. The company's innovations, such as NVIDIA Omniverse, transform traditional digital processes by enabling high-fidelity simulations and rendering tasks. Their applications span various industries, from autonomous vehicles using NVIDIA DRIVE to healthcare solutions with NVIDIA Clara, and AI-driven analytics and workflows.

📋 Description

• Provide hands-on technical mentorship to partners and customers on Nvidia GenAI stack. • Guide customers to develop 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.

🎯 Requirements

• 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. • Experience leading workshops, training sessions, and presenting technical solutions to diverse audiences.

🏖️ Benefits

• equity • benefits