Join NVIDIA as a Solutions Architect to own the evolution of Agentic AI for the enterprise. You will collaborate with top-tier enterprise software companies to build and deploy sophisticated AI-native systems, focusing on multi-agent coordination, RAG-integrated workflows, and accelerated inference. By mastering NVIDIA’s core technologies—NIM, NeMo Framework, Dynamo, and Nemo Agent Toolkit—you will guide partners through the complexities of performance optimization and production-grade deployment. As a trusted advisor, you’ll transform raw LLM capabilities into high-performance, industry-focused enterprise agents.
What you'll be doing:
Build complex agentic systems featuring multi-agent coordination, long-horizon reasoning, and advanced planning frameworks.
Develop full-scale solutions, including domain-specific enterprise agents and high-performance retrieval pipelines (RAG) spanning various data sources.
Optimize inference performance by bringing to bear GPU-accelerated frameworks and the full NVIDIA AI infrastructure stack.
Build hands-on PoCs and reference architectures that serve as the blueprint for production-grade generative AI pipelines.
Collaborate alongside Enterprise ISVs to integrate NVIDIA software into native platforms, accelerating the deployment of production workloads.
Collaborate with diverse internal teams to improve NVIDIA software through feedback from real-world implementations.
Empower partner engineering teams through technical workshops, deep-dive architecture reviews, and developer enablement.
Scale global expertise by crafting reusable assets and documentation that help field teams deploy agentic AI at scale.
What we need to see:
BS/MS/PhD in Computer Science, Electrical Engineering, AI/ML, or equivalent experience.
More than 5 years of experience in deep learning, machine learning, or distributed AI systems.
Strong programming and debugging experience in Python, C/C++, and Linux environments.
Background in using deep learning libraries like PyTorch or TensorFlow.
Hands-on experience building LLM and generative AI applications.
Experience working with agentic or multi-agent AI systems employing frameworks such as:
1. LangGraph
2. LlamaIndex
3. CrewAI
4. LangChain
5. OpenAI Agents SDK or similar orchestration frameworks
Experience building tool-using AI agents that interact with APIs, databases, and enterprise systems.
Ability to rapidly prototype AI applications and build scalable GPU-accelerated architectures.
Excellent interpersonal skills and the ability to collaborate with engineering teams, partners, and executive collaborators.
Ways to Stand Out from the Crowd:
Experience working with NVIDIA GPUs and AI software, such as NVIDIA NIM, NeMo Framework, NeMo Retriever, and NeMo Agent Toolkit.
Experience with LLM evaluation frameworks, benchmarking systems, and safety guardrails for agentic workflows.
Experience optimizing reasoning-focused LLMs through timely engineering, quantization, or benchmarking.
Familiarity with Kubernetes/OpenShift, CI/CD automation, and cloud-native deployment patterns for AI systems.
Experience with parallel or distributed computing environments and AI workloads optimized for GPUs.
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 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.Skills Required
- BS/MS/PhD in Computer Science, Electrical Engineering, AI/ML, or equivalent experience
- More than 5 years of experience in deep learning, machine learning, or distributed AI systems
- Strong programming and debugging experience in Python, C/C++, and Linux environments
- Background in using deep learning libraries like PyTorch or TensorFlow
- Hands-on experience building LLM and generative AI applications
- Experience working with agentic or multi-agent AI systems
- Experience building tool-using AI agents that interact with APIs, databases, and enterprise systems
- Ability to rapidly prototype AI applications and build scalable GPU-accelerated architectures
- Excellent interpersonal skills and the ability to collaborate with engineering teams, partners, and executive collaborators
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.
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.”
.jpg)
