AI Engineer – Technical Leader (Agentic AI & FPGA Systems)
Job Details:
Job Description:
Role Overview
We are seeking a highly experienced AI Technical Leader to drive the design and deployment of next-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 integrate software intelligence with hardware acceleration, with a strong focus on AI model optimization and deployment on FPGA.
Key Responsibilities
AI Architecture & System Design
Architect and implement end-to-end agentic AI systems (data → reasoning → action)
Design scalable multi-agent orchestration frameworks
Build enterprise-grade RAG 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 integrate state-of-the-art LLMs (open-source and proprietary)
Optimize prompting, memory, and reasoning strategies for performance and reliability
AI Model Optimization on FPGA (Core Focus)
Lead AI 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 to map AI models (e.g., CV, LLM inference) from PyTorch/Hugging Face to FPGA
Enable low-latency, high-efficiency inference pipelines on FPGA-based systems
Collaborate with hardware teams to align model 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 into EDA toolchains and hardware design flows
Drive innovations in software-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 of AI systems and semiconductor platforms
What Makes This Role Unique
Define agentic 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 shaping AI + 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:
Bridge LLM + Agentic AI + RAG with FPGA/ASIC system architecture
Drive AI model optimization and deployment on FPGA
Lead innovation in next-generation AI + hardware integrated systems
Qualifications:
Required
Strong experience in LLMs, agentic AI, or RAG systems
Proven track record in large-scale AI system architecture and deployment
Solid programming skills in Python/C++ and modern AI frameworks
Experience designing distributed or production AI systems
Strong system-level thinking across software and infrastructure
Preferred / Plus
Experience with FPGA, ASIC, or hardware acceleration platforms
Familiarity with FPGA development flows and toolchains (e.g., synthesis, compilation, HLS, runtime)
Experience with AI model optimization for hardware (quantization, compilation, deployment)
Exposure to EDA tools and semiconductor design workflows
Background in heterogeneous computing systems (CPU/GPU/FPGA)