Co-design and build production agentic systems hand-in-hand with a focused set of ISV Software Platform Architects.
Define technical objectives, timelines, and adoption strategy aligned to their critical business objectives.
Bring these to life across RAG, inference, multi-agent, and long-horizon workflows on NVIDIA's stack (NeMo, Nemotron, NIM, Dynamo, TensorRT-LLM) and ecosystem tools (vLLM, LangChain, MCP/A2A, and emerging agent frameworks).
Drive evaluations of NVIDIA libraries and ship the code and reference architectures that turn each engagement into durable software the ecosystem builds on.
Take charge of the technical engagement, ensuring software adoption through strong execution rigor.
Collaborate directly with NVIDIA Product, Engineering, NV Research, and Solution Architecture groups.
Share insights and impact to reach the best solution.
Develop technical breadth, and go deep in select areas as applicable: eg. Fine-tuning (PEFT, SFT), post-training and RL from verifiable rewards, Reasoning, RAG, Agent Evaluations and Observability, and Production inference.
Represent partner needs and architecture design to Product and Engineering teams.
Contribute to the product roadmap by articulating insights from large-scale enterprise environments and cross-industry patterns captured from ISV engagements.
Stay ahead in a fast paced field and NVIDIA's evolving models, libraries, and frameworks to keep your partners on the cutting edge.

12+ years in technical Engineering, Product, or Solutions roles across enterprise software and production AI, including customer
or partner-facing work.
Masters 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 GenAI systems, e.g. advanced RAG, multi-agent architectures/harnesses, and production LLM 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 (eg. LangChain, inference engines such as vLLM/TensorRT-LLM, NVIDIA NeMo, vector DBs, MCP/A2A).
Direct experience with every tool is not required but foundational breadth is desired.
Strong grasp of enterprise deployment realities: MLOps/LLMOps, Kubernetes and Docker, and Security, Compliance, and Governance.
Strong executive-caliber communication and deep curiosity for emerging AI technologies.
High ownership and initiative, with the relationship-building instinct to keep internal and external collaborators aligned.

Applied Agentic AI Lead, Partner Co-Design

Key skills