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Senior AI Engineer
Sin especificarTiempo completoAI EngineeringSemiconductors
Build production agent workflows around hardware network simulation tools. Shape the context agents use, improve runtime performance, and develop evaluation, observability, and reliability practices in partnership with network architects.
Responsabilidades
- Build multi-step agent workflows for hardware network simulation and analysis.
- Develop context pipelines that ground agents in simulation models, specifications, design documents, and source code.
- Work with network architects to translate their workflows into agent tools.
- Optimize simulation tooling performance, compute cost, and end-to-end latency.
- Create evaluation and regression testing for agent workflows.
- Build observability into agent runs and investigate failures.
- Promote secure and reliable workflows through data handling, access control, and interaction guidelines.
- Resolve complex integration issues between agents and internal tools.
Requisitos
- Bachelor’s degree or higher in computer science, computer engineering, or a related field, or equivalent experience.
- At least 5 years of hands-on software engineering experience, including ownership of production systems from design through deployment.
- Expert-level C++ programming skills and strong Python skills.
- Strong understanding of hardware and full-stack performance, including memory, I/O, networking, accelerators, and bottlenecks.
- Practical knowledge of building systems around AI models, 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.
Se valora
- Experience with hardware simulation software, networking, or work alongside silicon, systems, or architecture teams.
- Experience with inference serving engines such as vLLM, TensorRT-LLM, or Triton Inference Server and their internals.
- Performance profiling, GPU programming, or experience building or fine-tuning generative models.
- Experience creating agent workflows, tools, or context pipelines adopted by engineering teams.