Senior AI Engineer – Network Architecture

NVIDIA

. Build agentic workflows, including loops, graphs, and multi-step pipelines, for hardware network simulation and analysis .

Posted 9/29/2026full-timeTel Aviv • IsraelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in building and optimizing agent workflows for hardware network simulation, with strong programming skills in C++ and Python. Possesses a deep understanding of AI model systems and inference serving, along with hands-on experience in software engineering and production system ownership.

Highest-signal resume keywords

C++ ProgrammingPython ProgrammingAgent WorkflowsAI Model SystemsInference Serving

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Hard Skills

Software EngineeringHardware SimulationNetworking ProtocolsGPU ProgrammingRegression TestingSimulation Tooling OptimizationContext RetrievalData Handling GuidelinesIntegration Issue ResolutionProduction System Ownership

Soft Skills

CollaborationProblem SolvingOwnership

Tools & Technologies

VLLMTensorRT-LLMTriton Inference Server

Industry Keywords

Network SimulationAgent WorkflowsInference Serving EnginesCompute Cost OptimizationEnd-to-End Latency

Tech Stack

Tools & technologies

PythonSwitchingC++

About the role

Key responsibilities & impact

Build agentic workflows, including loops, graphs, and multi-step pipelines, for hardware network simulation and analysis
Engineer workflow context from simulation models, specifications, design documents, and source code
Collaborate with network architects to understand workflows and translate them into agent workflows
Optimize simulation tooling runtime performance, including execution time, compute cost, and end-to-end latency
Define evaluation and regression testing for agent workflows
Build observability across agent runs, including actions, failures, and causes
Champion secure and reliable agent workflow guidelines covering data handling, access control, and interaction boundaries
Solve sophisticated integration issues between agents and internal tooling

Requirements

What you’ll need

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
Demonstrated ownership of production systems from design through deployment
Expert-level programming skills in C++
Strong Python skills
Strong understanding of the full stack, including hardware: memory, I/O, networking, and accelerators
Practical knowledge of AI model systems, including agent loops, tool interfaces, context retrieval and management, and common failure modes
Understanding of inference serving, including request lifecycle, batching, caching, and throughput, latency, and cost tradeoffs
Experience with hardware simulation software is advantageous
Networking experience with protocols, fabrics, switching, or RDMA is advantageous
Experience with inference serving engines such as vLLM, TensorRT-LLM, or Triton Inference Server is advantageous
Knowledge of KV cache, batching and scheduling, quantization, profiling, and GPU programming is advantageous
Hands-on experience building or fine-tuning LLMs or other generative models is advantageous
Experience with agent workflows, tooling, or context pipelines adopted by engineering teams is advantageous

Benefits

Comp & perks

Equal-opportunity employer
Commitment to fostering a diverse work environment