Senior Solutions Architect, Gen AI

About the Role

Key Responsibilities

• Provide hands-on technical mentorship to partners and customers on Nvidia GenAI stack.
• Guide customers to develop and deploy Agentic AI workflows on NVIDIA platforms, demonstrating the benefits of accelerated computing software and hardware.
• Build demonstrations and proof-of-concepts (POCs) for solutions addressing critical business needs.
• Help draft requirements for missing features to unblock progress at customers and partners.
• Educate customers on new NVIDIA GenAI technologies and platforms.
• Prepare and deliver technical training presentations and workshops.
• Build collateral such as notebooks and blogs applied to industry use-cases.
• Partner with NVIDIA engineering, product, and 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 model architectures and able to articulate their computational complexities.
• Experience deploying and optimizing LLM models for inference in production environments using inferencing engines such as vLLM, TRT-LLM, SGLang, etc.
• Expertise in training and fine-tuning LLMs using frameworks like TensorFlow, PyTorch, or Hugging Face Transformers.
• Solid understanding of GPU cluster architecture and parallel processing for accelerated model training and inference.
• Experience with basic DevOps tools such as Docker, Kubernetes, GitLab, Linux Command Line, Shell, etc.
• Excellent communication and teamwork skills, with the ability to explain complex technical concepts to diverse audiences.
• Experience leading workshops, training sessions, and presenting technical solutions.

Nice to Have

• Experience with Agentic AI frameworks, tools, and protocols such as LangChain, LangGraph, MCP.
• Understanding of Multimodal LLMs, VLMs, etc.
• Experience deploying LLM models at scale on cloud providers like AWS, Azure, GCP.
• Proven ability to profile and optimize inference latency, throughput, memory, and I/O utilization.
• Mathematical understanding of parallelisation techniques in Gen AI.

Qualifications

• Educational background with a Master's or Ph.D. in relevant fields.

Benefits & Perks

• Base salary range of 184,000 USD - 356,500 USD, determined by location, experience, and comparable roles.
• Eligibility for equity and benefits.