9 часов назад
AI Engineer – Technical Leader (Agentic AI & FPGA Systems)
Мэтч & Сопровод
Для мэтча с этой вакансией нужен Plus
Описание вакансии
Текст:
TL;DR
AI Engineer – Technical Leader (Agentic AI & FPGA Systems) (Agentic AI, LLM, RAG, FPGA): Designing and deploying agentic AI systems that integrate LLM reasoning, retrieval, orchestration, and FPGA/ASIC hardware acceleration with an accent on model optimization, hardware-aware architecture, and scalable inference. Focus on quantization, graph compilation, operator fusion, heterogeneous CPU/GPU/FPGA partitioning, and mapping PyTorch or Hugging Face models to low-latency FPGA pipelines.
Location: Shanghai, China
Company
develops semiconductor and FPGA-based computing platforms for intelligent and hardware-accelerated systems.
What you will do
- Architect and implement end-to-end agentic AI systems spanning data, reasoning, action, and hardware acceleration.
- Design multi-agent orchestration frameworks and enterprise-grade RAG pipelines for knowledge integration and retrieval.
- Evaluate and integrate open-source and proprietary LLMs, optimizing prompting, memory, and reasoning strategies.
- Lead AI model optimization and deployment on FPGA platforms, including quantization, graph compilation, operator fusion, and model partitioning.
- Map computer vision and LLM inference models from PyTorch or Hugging Face to FPGA-based low-latency pipelines.
- Set technical direction, lead architecture reviews, mentor engineers, and coordinate software, infrastructure, and hardware teams.
Requirements
- Strong experience with LLMs, agentic AI, or RAG systems.
- Proven experience in large-scale AI system architecture and production deployment.
- Strong Python or C++ programming skills and experience with modern AI frameworks.
- Experience designing distributed or production AI systems with strong system-level thinking across software and infrastructure.
- Ability to work across AI systems, FPGA/ASIC architecture, and heterogeneous CPU/GPU/FPGA platforms.
Nice to have
- Experience with FPGA, ASIC, or other hardware acceleration platforms.
- Familiarity with FPGA synthesis, compilation, HLS, and runtime toolchains.
- Experience with hardware-oriented model quantization, compilation, and deployment.
- Exposure to EDA tools, semiconductor design workflows, and heterogeneous computing systems.
Culture & Benefits
- Full-time regular employment.
- Work on AI reasoning systems, FPGA acceleration, and next-generation computing platforms.
- Opportunity to shape software-hardware co-design and AI–semiconductor integration.
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