NVIDIASenior Solutions Architect, Retail
AI in this role
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.
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.How we rate this
Senior Solutions Architect, Retail at NVIDIA rates 96 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.
Builds AI. The job is building AI systems.
- ●●●● Builds AI80 to 100
- ●●●○ Works on AI60 to 79
- ●●○○ Uses AI40 to 59
- ●○○○ Little AI0 to 39
Levels come from how often the tools, models and workflows of the role are named in the posting itself. Open the description and count.
Prepare for this job
A free preview built only from this posting: what it asks for, what you could be asked in an interview, and how to adjust your resume.
Skills and AI tools this role asks for
Questions you could be asked
- How would you design a retrieval step so the model answers from real data instead of guessing?
- How do you decide when an AI agent can act on its own versus asking for approval first?
- Walk me through fine-tuning a model: what data did you use, and how did you check the result?
- How do you decide that one model's output is better than another's for a given task?
- Walk me through how you've used OpenAI in your day-to-day work.
Adapt your resume
- List these exact terms on your resume: RAG, AI Agents, Fine Tuning, AI Evaluation, and OpenAI. An applicant tracking system matches the wording, not the idea.
- Attach one line of real, concrete experience to at least one of them — a tool named with nothing behind it rarely survives a human read.
- Lead with what you built, trained or shipped — this role is judged on the AI system itself, not the tools around it.
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