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Senior AI Engineer and Agentic Platforms - Network Architecture

NVIDIA· Architect
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Hybrid (Tel Aviv, Israel), Hybrid (Yokneam, Israel)Full-timePosted 4mo ago

About the role

Senior software engineer role focused on building agentic workflows for a hardware network simulation environment, optimizing runtime performance, and ensuring secure, observable agent runs within NVIDIA’s Network Architecture group.

Responsibilities

  • Build agentic workflows — loops, graphs, and multi-step pipelines — that carry real hardware network simulation and analysis work end to end.
  • Engineer the context these workflows run on, turning simulation models, specifications, design documents, and source code into context that makes agents accurate in our domain.
  • Work closely with network architects to understand their workflows and translate them into agent workflows they use daily.
  • Optimize the runtime performance of our simulation tooling on these platforms, including execution time, compute cost, and end-to-end latency.
  • Define evaluation and regression testing for agent workflows, so that changes to a prompt, a graph, or a context source are measurable.
  • Build observability across agent runs: what the agent did, where it failed, and why.
  • Champion guidelines for secure and reliable agent workflows, including data handling, access control, and interaction boundaries.
  • Serve as a key technical resource for solving sophisticated integration issues between agents and internal tooling.

Qualifications

  • B.Sc. or above in Computer Science, Computer Engineering, or a related field, or equivalent experience.
  • 5+ years of hands-on experience in software engineering, with demonstrated ownership of production systems from design through deployment.
  • Expert-level programming skills in C++, with strong Python skills alongside it.
  • Strong understanding of the full stack, including hardware: memory, I/O, networking, accelerators, and where real performance bottlenecks occur.
  • Current, practical knowledge of how to build systems around AI models: agent loops, tool interfaces, context retrieval and management, and common failure modes.
  • Understanding of inference serving, including request lifecycle, batching, caching, and the tradeoffs between throughput, latency, and cost.

Skills

Required:C++PythonSystem design and ownership of production systems
Last updated Oct 2, 2026. Interested? Apply on the company site.

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