Engagement Tech Lead, Agentic AI

Job Description:

  • Lead the technical engagement with a focused set of ISV partners, working closely with their Software Platform Architects.
  • Together you'll set the objectives, timelines, and adoption plan for each account.
  • Stay hands-on: design and ship the code, methods, and reference architectures that bring RAG, inference, and multi-agent, long-horizon workflows to life on our stack (NeMo, Nemotron, NeMo Agent Toolkit, NIM, Dynamo, TensorRT-LLM) and open tools (vLLM, LangChain, vector DBs, MCP, A2A).
  • Lead the technical strategy and execution: run the cadence, track adoption, and share what you learn with our Product, Engineering, Research, and Solution Architecture teams to land the best solution.
  • Build breadth across the agentic AI lifecycle, with depth in a few areas that fit you: fine-tuning (PEFT, SFT), post-training and RL from verifiable rewards, reasoning, advanced RAG, multi-agent workflows, skills/harness engineering, agent evaluation and observability, and production inference.
  • Be the voice of the partner inside NVIDIA. Bring their needs and architecture to our Product, Engineering, and Research teams, and help shape the roadmap with what you see across industries.
  • Grow deep expertise in agentic platform architecture, and keep up with our fast-moving field and stack so you can give partners the best guidance.

Requirements:

  • 12+ years in technical Product, Engineering, 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: advanced RAG, multi-agent architectures, and production LLM deployments.
  • You enjoy leading technical engagements with customers, earning the trust of senior engineers, architects, and executives, and bringing a cross-functional team along.
  • Proficiency in Python and PyTorch and the modern agent/LLM stack (LangChain, an inference engine like vLLM or TensorRT-LLM, vector DBs, MCP/A2A).
  • Direct experience with every tool is not required.
  • Solid grasp of enterprise deployment: MLOps/LLMOps, Kubernetes and Docker, and security, compliance, and governance.
  • The range to research, prototype, and collaborate across teams, and execution rigor to carry the best solution through to production.

Benefits:

  • equity
  • benefits
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