About NVIDIA
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.
Role Overview
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. 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.
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. As an NVIDIAN, you’ll be immersed in a diverse, supportive environment where everyone is inspired to do their best work.
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.
