Nvidia Solutions Architect Agentic AI 

United States, Texas 

281663058

The Agentic AI team mission is to deliver innovative and optimized AI agents using the latest techniques including Test Time Compute, Reinforcement Learning, inference optimization and model fine-tuning. We specialize on engineering new solutions to fit our customers needs by integrating their enterprise data sources into meaningful agentic applications.

You’ll work with agentic frameworks to develop applications that retrieve and generate insights from enterprise data, including text, code, and images. Your focus will be on creating high-impact solutions such as deep research assistants, multi-modal dialogue systems, and task-specific agents that support a wide range of enterprise workflows. You’ll be deeply engaged with engineering teams, stay ahead of the latest AI advancements, and apply strong technical judgment to everything you deliver.

Provide direct feedback from these first-time implementations to improve our software products and scale knowledge by educating vertical teams and building communities on NVIDIA AI software products!

Strong foundational expertise, from a BS, MS, or Ph.D. degree in Engineering, Mathematics, Physics, Computer Science, Data Science, or similar (or equivalent experience).

5+ years experience demonstrating an established track record in Deep Learning and Machine Learning. Strong software engineering and debugging skills, including experience with Python, C/C++, and Linux. Experience with GPUs as well as expertise in using deep learning frameworks such as TensorFlow or PyTorch.

Proficiency in rapid prototyping using Python with strong foundational knowledge of data structures, algorithms, and software engineering principles.

Experience with building advanced multi-agent systems, using libraries like LangGraph, LlamaIndex, CrewAI.

Ability to multitask effectively in a dynamic environment, as well as clear written and oral communications skills with the ability to effectively collaborate with executives and engineering teams.

Expertise in building evaluation harnesses, success metrics, automated testing pipelines, and guardrail frameworks to ensure agentic AI workflows are safe, reliable, and production-ready.

Skilled in fine-tuning and optimizing reasoning-focused LLMs and SLMs, including prompt engineering, quantization, and benchmarking.

Experience developing production-grade deployment patterns usingKubernetes/OpenShift,CI/CD automation, and secure cloud-native infrastructure.