Job description

Interested in developing innovative Agentic AI solutions with the world's top Retail, CPG, and QSR companies? Join NVIDIA as a Solutions Architect to own the evolution of Agentic AI for the enterprise. You will collaborate with top-tier Retail 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. • 8+ 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: LangGraph, LlamaIndex, CrewAI, LangChain, 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. 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. • Background with LLM evaluation frameworks, benchmarking systems, and safety guardrails for agentic workflows. • Experience with pre-training/fine-tuning techniques like SFT, LoRA, DPO, PPO, GRPO, DAPO, or RLVF • Experience optimizing reasoning-focused LLMs through timely engineering, quantization, or benchmarking. • Background with parallel or distributed computing environments and AI workloads optimized for GPUs. Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 184,000 USD - 287,500 USD for Level 4, and 224,000 USD - 356,500 USD for Level 5. You will also be eligible for equity and benefits . Applications for this job will be accepted at least until October 10, 2026. This posting is for an existing vacancy. NVIDIA uses AI tools in its recruiting processes. NVIDIA is committed to fostering an inclusive 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.

Requirements

Must-haves

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.
BS/MS/PhD in Computer Science, Electrical Engineering, AI/ML, or equivalent experience.
8+ 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: LangGraph, LlamaIndex, CrewAI, LangChain, 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.
Experience working with NVIDIA GPUs and AI software, such as NVIDIA NIM, NeMo Framework, NeMo Retriever, and NeMo Agent Toolkit.
Background with LLM evaluation frameworks, benchmarking systems, and safety guardrails for agentic workflows.
Experience with pre-training/fine-tuning techniques like SFT, LoRA, DPO, PPO, GRPO, DAPO, or RLVF
Experience optimizing reasoning-focused LLMs through timely engineering, quantization, or benchmarking.
Background with parallel or distributed computing environments and AI workloads optimized for GPUs.
You will also be eligible for equity and benefits .
Applications for this job will be accepted at least until October 10, 2026.
This posting is for an existing vacancy.

Nice-to-haves

None flagged.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aillmmlmachine learningpython

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