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NVIDIA

Designs the chips and software that power AI

Applied Agentic AI Lead - Partner Co-Design

AI Application EngineeringConfirmed live in the last 24 hours
Full-timeRemote

About the Job

Requirements

  • At least 12 years in technical Engineering, Product, or Solutions roles across enterprise software and production AI, including customer- or partner-facing work.
  • A Master's or PhD in Computer Science, Electrical Engineering, or equivalent experience.
  • A strong AI/ML and Deep Learning foundation, with hands-on experience building enterprise-grade Generative AI systems, such as advanced Retrieval-Augmented Generation, multi-agent architectures or harnesses, and production Large Language Model evaluations and deployments.
  • Ability to swiftly research, prototype, and collaborate with multiple teams to arrive at the best technical solution for new and evolving customer scenarios, driving to completion with ownership of large projects.
  • Fluency in Python and the modern agent/LLM stack, such as LangChain, inference engines such as vLLM/TensorRT-LLM, NVIDIA NeMo, vector databases, and MCP/A2A; direct experience with every tool is not required, but foundational breadth is desired.
  • Strong grasp of enterprise deployment realities, including MLOps/LLMOps, Kubernetes and Docker, and security, compliance, and governance.
  • Strong executive-caliber communication and deep curiosity for emerging AI technologies, with high ownership and initiative and the relationship-building instinct to keep internal and external collaborators aligned.

Responsibilities

  • Co-design and build production agentic systems with a focused set of ISV Software Platform Architects; define technical objectives, timelines, and adoption strategy aligned to critical business objectives.
  • Develop production systems across Retrieval-Augmented Generation, inference, multi-agent, and long-horizon workflows on NVIDIA's stack and ecosystem tools; drive evaluations of NVIDIA libraries and ship code and reference architectures.
  • Take charge of technical engagements and ensure software adoption through strong execution rigor; collaborate directly with NVIDIA Product, Engineering, NVIDIA Research, and Solution Architecture groups, and share insights and impact to reach the best solution.
  • Develop technical breadth and depth in applicable areas including fine-tuning (PEFT, SFT), post-training and reinforcement learning from verifiable rewards, reasoning, Retrieval-Augmented Generation, agent evaluations and observability, and production inference.
  • Represent partner needs and architecture design to Product and Engineering teams, and contribute to the product roadmap by articulating insights from large-scale enterprise environments and cross-industry patterns captured from ISV engagements.
  • Stay current with NVIDIA's evolving models, libraries, and frameworks to keep partners on the cutting edge.

Desired Qualifications

  • Track record of influencing sophisticated product decisions through trusted partner relationships, with empathy for customer needs and an instinct for translating them into scalable platform improvements.
  • Proactive anticipation of market trends and advocacy for innovation inside and outside the organization, with in-depth knowledge of the ISV and enterprise agent-platform landscape.
  • Proven agility in high-stakes environments, delivering successful partner outcomes in a high-velocity agentic AI landscape.
  • Strong growth and solution outlook, and the ability to build deep trust with engineers, executives, and cross-functional teams at both NVIDIA and partner organizations.

About the Company

NVIDIA

Designs the chips and software that power AI

NVIDIA designs the chips and software that power artificial intelligence and sells complete data center systems to cloud providers, AI labs, and other businesses, along with GeForce graphics cards for gamers. Its GPUs, chips that perform thousands of calculations at once, became the main hardware for AI after researchers used them to train the AlexNet image-recognition model in 2012. NVIDIA connects many GPUs with its own processors and networking gear into servers and rack-sized computers, and developers program them through its CUDA software. These systems bring in most of its sales, and it also makes chips for cars and robots. Jensen Huang co-founded NVIDIA in 1993, and it is based in Santa Clara, California.

Company size
10,001+
Company stage
IPO
Headquarters
Santa Clara, CA
Founded
1993

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