NVIDIA
Applied Agentic AI Lead, Partner Co-Design
$224K–$431K
US, CA, Santa Clara +1 · Full-time · Posted Sep 10, 2026
- Location
- US, CA, Santa Clara
US, Remote - Experience
- 12+ years in technical Engineering, Product, or Solutions roles across enterprise software and production AI, including customer- or partner-facing work.
- Education
- Master’s or PhD in Computer Science, Electrical Engineering, or equivalent experience.
About the job
Leads technical co-design with strategic enterprise ISV partners to build production agentic AI systems, drive adoption of NVIDIA’s software stack, and inform product roadmaps.
What you'll do
- Co-design and build production agentic systems, setting technical objectives, timelines, and adoption strategies with partners.
- Evaluate NVIDIA libraries and deliver code and reference architectures for RAG, inference, multi-agent, and long-horizon workflows.
- Represent partner needs to NVIDIA Product, Engineering, Research, and Solutions Architecture teams, contributing deployment insights to product roadmaps.
What they ask for
- Strong AI/ML and deep learning foundation, with hands-on experience building enterprise-grade generative AI systems.
- Fluency in Python and modern agent/LLM tools; enterprise deployment knowledge including MLOps/LLMOps, Kubernetes, Docker, and security, compliance, and governance.
- Ability to research, prototype, collaborate across teams, and own large projects through completion.
- Executive-caliber communication, initiative, and ability to align internal and external collaborators.
Nice to have
- Record of influencing product decisions through trusted partner relationships and translating customer needs into platform improvements.
- Knowledge of the ISV and enterprise agent-platform landscape, and success delivering partner outcomes in high-stakes environments.
Pay
224,000 USD - 356,500 USD (base salary, Level 5); 272,000 USD - 431,250 USD (base salary, Level 6)
Tools
Python, LangChain, vLLM, TensorRT-LLM, NVIDIA NeMo, vector DBs, MCP/A2A, Kubernetes
AI focus
Co-design and build production generative and agentic AI systems, including RAG, multi-agent workflows, inference, fine-tuning, post-training, reinforcement learning, evaluation, observability, and deployment.
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