AI Engineer – Technical Leader (Agentic AI & FPGA Systems)

Job Details:

Job Description:

Role Overview

We are seeking a highly experiencedAI Technical Leader to drive the design and deployment ofnext-generation agentic AI systems optimized for FPGA/ASIC platforms.

This role sits at the convergence of:

• Large Language Models (LLMs)

• Agentic AI & autonomous systems

• Retrieval-Augmented Generation (RAG)

• Hardware-aware AI system design (FPGA / ASIC / EDA)

You will define system architecture, lead technical strategy, and deliver scalable AI solutions that tightly integratesoftware intelligence with hardware acceleration, with a strong focus onAI model optimization and deployment on FPGA.

Key Responsibilities

AI Architecture & System Design

• Architect and implementend-to-end agentic AI systems (data → reasoning → action)

• Design scalablemulti-agent orchestration frameworks

• Build enterprise-gradeRAG pipelines for knowledge integration and retrieval

• Define system-level architecture bridging cloud, edge, and hardware acceleration

Agentic AI & LLM Development

• Develop advanced agent workflows, including:

  • Tool-using agents
  • Planning and reasoning agents
  • Multi-agent collaboration systems

• Evaluate and integratestate-of-the-art LLMs (open-source and proprietary)

• Optimizeprompting, memory, and reasoning strategies for performance and reliability

AI Model Optimization on FPGA (Core Focus)

• LeadAI model optimization and deployment on FPGA platforms, including:

  • Quantization (INT8 / BF16 / mixed precision)
  • Graph compilation and operator fusion
  • Model partitioning across CPU/GPU/FPGA
  • Memory and bandwidth optimization

• Develop workflows tomap AI models (e.g., CV, LLM inference) from PyTorch/Hugging Face to FPGA

• Enablelow-latency, high-efficiency inference pipelines on FPGA-based systems

• Collaborate with hardware teams to alignmodel architecture with FPGA constraints (DSP, BRAM, interconnect)

Hardware-Aware AI Co-Design

• Co-design AI systems with:

  • FPGA acceleration
  • ASIC platforms
  • Heterogeneous compute architectures (CPU/GPU/FPGA)

• Integrate AI workflows intoEDA toolchains and hardware design flows

• Drive innovations insoftware-hardware co-optimization

Framework & Platform Development

• Build internal AI platforms leveraging:

  • LangChain
  • LlamaIndex
  • Custom agent orchestration frameworks​

• Develop reusable components for:

  • Memory management
  • Tool integration
  • Knowledge retrieval and indexing​

• Enable scalable deployment pipelines for AI applications on FPGA

Technical Leadership

• Lead architecture design reviews and set technical direction

• Mentor senior engineers and guide cross-functional teams

• Drive innovation at the intersection ofAI systems and semiconductor platforms

What Makes This Role Unique

• Defineagentic AI architecture for hardware-native AI systems

• Work at the frontier of:

  • AI reasoning systems

  • FPGA-based acceleration

  • Next-generation computing platforms

​

• High-impact role shapingAI + semiconductor convergence

Impact

You will enable a new class of intelligent systems where AI agents are tightly coupled with hardware, delivering:

• Real-time decision-making pipelines

• Efficient, scalable AI deployment on FPGA

• End-to-end automation from model to hardware execution

Summary

This role is ideal for a senior technical leader who can:

• BridgeLLM + Agentic AI + RAG withFPGA/ASIC system architecture

• DriveAI model optimization and deployment on FPGA

• Lead innovation in next-generationAI + hardware integrated systems

Qualifications:

Required

• Strong experience inLLMs, agentic AI, or RAG systems

• Proven track record inlarge-scale AI system architecture and deployment

• Solid programming skills inPython/C++ and modern AI frameworks

• Experience designingdistributed or production AI systems

• Strong system-level thinking across software and infrastructure

Preferred / Plus

• Experience withFPGA, ASIC, or hardware acceleration platforms

• Familiarity withFPGA development flows and toolchains (e.g., synthesis, compilation, HLS, runtime)

• Experience withAI model optimization for hardware (quantization, compilation, deployment)

• Exposure toEDA tools and semiconductor design workflows

• Background inheterogeneous computing systems (CPU/GPU/FPGA)

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