Senior AI Engineer and Agentic Platforms - Network Architecture

NVIDIA is at the forefront of the AI revolution, delivering brand new accelerated compute platforms for global impact. Our Network Architecture group is seeking a talented and motivated Sr. Software Engineer to build the agentic workflows that our architects use in their daily work. The software at the center of this role is our hardware network simulation environment — you will design multi-step agent workflows over it, engineer the context that grounds them in our own specifications and source code, and optimize their runtime performance. If you are passionate about building the practical infrastructure that brings intelligent agents to life, we want to hear from you.

What You'll Be Doing

• 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 our 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.
  
  

• 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.
  
  

• Experience writing hardware simulation software — network, system, or architectural simulators, models, or testbenches.
• Networking experience — protocols, fabrics, switching, or RDMA — and experience working alongside silicon, systems, or architecture teams.
• Hands-on experience with inference serving engines such as vLLM, TensorRT-LLM, or Triton Inference Server, including low-level internals such as KV cache, batching and scheduling, and quantization, and related performance work such as profiling and GPU programming.
• Hands-on experience building or fine-tuning LLMs or other generative models.
• Agent workflows, tooling, or context pipelines adopted by other engineering teams.