Solution Architect - Agentic AI

Posted Yesterday
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5 Locations
In-Office or Remote
Senior level
Artificial Intelligence • Computer Vision • Hardware • Robotics • Metaverse
The Role
Designs and demonstrates enterprise-ready agentic AI architectures using NVIDIA technologies. Leads technical discovery, builds proofs of concept, develops reference architectures, optimizes inference and workloads, and supports secure deployment across local, on-premises, hybrid, sovereign, and cloud environments. Partners with customers to integrate orchestration, retrieval, model serving, sandboxing, governance, and GPU infrastructure while creating reusable playbooks, benchmarks, workshops, and enablement assets.
Summary Generated by Built In

NVIDIA has been transforming computer graphics, PC gaming, and accelerated computing for more than 25 years. It’s a unique legacy of innovation that’s fueled by great technology—and amazing people.  

Today, we’re tapping into the unlimited potential of AI to define the next era of computing. An era in which our GPU acts as the brains of computers, robots, and self-driving cars that can understand the world. Doing what’s never been done before takes vision, innovation, and the world’s best talent. As an NVIDIAN, you’ll be immersed in a diverse, supportive environment where everyone is inspired to do their best work. Come join the team and see how you can make a lasting impact on the world. 


Join us at NVIDIA, where we are pushing the boundaries of AI to develop the future of computing! Our Agentic AI Applications and Specialized Agents team is leading this transformation, driving innovation in AI-native software. As a Solutions Architect, you’ll play a pivotal role in helping our partners build enterprise-ready agentic AI products on the NVIDIA stack. This outstanding opportunity allows you to work with innovative technology and collaborate with individuals known for their creativity and problem-solving skills in the industry. You’ll be instrumental in making NVIDIA the technical foundation for AI application wrappers, orchestrating agents, and specialized workflows. 


What You’ll Be Doing:

  • Lead deep technical discovery with AI application wrapper partners to understand their product architecture, agent topology, and orchestration model. 
  • Build reference architectures that integrate NVIDIA NIM, Nemotron, model routing, Dynamo, NeMo, AI-Q, CUDA-X libraries, OpenShell, and NemoClaw. 
  • Build hands-on proofs of concept to demonstrate how partners can evolve from general-purpose AI wrappers to specialized, enterprise-ready agent platforms. 
  • Use DGX Spark for local agent demos, secure sandboxed workflows, developer playbooks, pilot environments, and desktop-scale benchmarking for agentic use cases. 
  • Standardize on NIM plus Dynamo for serving patterns in agentic applications, ensuring fast time to first token and multi-turn reasoning retention. 
  • Help partners integrate secure runtime patterns including sandboxing, policy enforcement, and human-in-the-loop control points. 
  • Support multi-stage deployment paths from local DGX Spark development to on-prem, sovereign, hybrid cloud, or AI Factory infrastructure rollouts. 
  • Optimize end-to-end agentic workloads for latency, throughput, retrieval quality, cost efficiency, and GPU utilization. 
  • Develop reusable enablement assets like reference architectures, workshop content, benchmark reports, and playbooks for agentic application wrappers. 

What We Need To See:

  • BS, MS, PhD or equivalent experience in Computer Science, Electrical/Computer Engineering, Physics, Mathematics, or a related engineering field—or equivalent practical experience.
  • 8+ years in building, deploying, or supporting production LLM or agentic AI systems. 
  • Strong Python programming and practical proficiency with TypeScript or JavaScript. 
  • Practical knowledge of orchestration frameworks, agent frameworks, RAG platforms, workflow automation systems, or AI application builders. 
  • Familiarity with model serving and inference optimization, including NIM and Dynamo. 
  • Experience with Kubernetes, Helm, containers, and GPU-backed workloads in cloud, hybrid, or on-prem environments. 
  • Ability to debug across the full stack: application code, APIs, model endpoints, orchestration layers, retrieval systems, infrastructure, and security controls. 
  • Expertise in working directly with customers in architectural roles and the capability to impact both creators and corporate decision-makers. 
  • Comfort with uncertainty and the ability to rapidly transition from idea to proof of concept. 

Ways To Stand Out From The Crowd:

  • Practical experience working directly with NVIDIA NIM, Nemotron, NeMo, Dynamo, AI-Q, OpenShell, NemoClaw, or NVIDIA AI Enterprise. 
  • Experience with platforms like Lovable, n8n, Haystack, LangChain, LlamaIndex, AutoGen, CrewAI, or MCP-based systems. 
  • Expertise in secure agent runtime build, sandboxing, tool permissioning, confidential computing, policy-as-code, or enterprise governance controls. 
  • Familiarity with DGX Spark as a local AI development platform, or experience crafting developer playbooks bridging desktop prototyping to data-center deployment. 
  • Background in sovereign AI, regulated enterprise environments, or private/on-prem deployment models. 

NVIDIA is widely considered to be one of the technology world’s most desirable employers. We have some of the most forward-thinking and hardworking people in the world working for us. If you're creative and autonomous, we want to hear from you! 

NVIDIA is committed to fostering a diverse 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. 

If you are passionate about NVIDIA technology, we should talk! 

 

Skills Required

  • Bachelor’s, master’s, or doctoral degree in Computer Science, Electrical or Computer Engineering, Physics, Mathematics, or a related engineering field, or equivalent practical experience
  • 8+ years of experience building, deploying, or supporting production LLM or agentic AI systems
  • Strong Python programming skills
  • Practical proficiency with TypeScript or JavaScript
  • Practical knowledge of orchestration frameworks, agent frameworks, RAG platforms, workflow automation systems, or AI application builders
  • Familiarity with model serving and inference optimization, including NIM and Dynamo
  • Experience with Kubernetes, Helm, containers, and GPU-backed workloads in cloud, hybrid, or on-premises environments
  • Ability to debug application code, APIs, model endpoints, orchestration layers, retrieval systems, infrastructure, and security controls
  • Experience working directly with customers in architectural roles and influencing technical and corporate decision-makers
  • Ability to operate effectively amid uncertainty and rapidly transition from ideas to proofs of concept
  • Experience with NVIDIA NIM, Nemotron, NeMo, Dynamo, AI-Q, OpenShell, NemoClaw, or NVIDIA AI Enterprise
  • Experience with Lovable, n8n, Haystack, LangChain, LlamaIndex, AutoGen, CrewAI, or MCP-based systems
  • Expertise in secure agent runtimes, sandboxing, tool permissioning, confidential computing, policy-as-code, or enterprise governance controls
  • Familiarity with DGX Spark or developer playbooks bridging desktop prototyping to data-center deployment
  • Background in sovereign AI, regulated enterprise environments, or private/on-premises deployment models

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.

  • Equity Value & Accessibility — Equity awards and a discounted ESPP are highlighted as core parts of total compensation, enabling employees to share in the company’s success. Stock-based compensation and the two-year lookback ESPP are consistently described as especially valuable.
  • Healthcare Strength — Health coverage is portrayed as robust, with comprehensive medical, dental, and vision options alongside mental health support and on-site care resources. Employer HSA contributions and wellness perks reinforce the depth of the offering.
  • Retirement Support — Retirement programs are depicted as strong, featuring a meaningful 401(k) match with Roth options and support for Mega Backdoor Roth contributions. These elements position long-term savings as a notable advantage of the total rewards package.

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The Company
HQ: Santa Clara, CA
21,960 Employees
Year Founded: 1993

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.”

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