Summary
Rajeev Nayak is an R&D engineer with 14 years of experience specializing in AI, embedded systems, and semiconductor packaging, currently building database systems, edge AI solutions, and AI-driven process tools. He holds an M.S. in Electrical Engineering from National Sun Yat-Sen University with a focus on hardware-based AI and lightweight object detection, and blends neural network engineering (PyTorch/TensorFlow) with Verilog-based hardware design. At 3S Silicon Tech he has delivered practical tooling—recipe management and outcome prediction, EDA utilities, refurbished IPCs with self-hosted GitLab, and an offline RAG agent for local AI inference—demonstrating an emphasis on deployable, production-ready systems. His projects span underwater image enhancement with VAE autoencoders, YOLOv5-based edge object detection, brainwave classification, and low-power DC-DC ramp generator design, reflecting a rare mix of signal processing, ML, and power-electronics experience. Rajeev is pragmatic about constrained environments, evidenced by Yocto-based BeagleBone OS work and optimizations for embedded AI, and he favors self-contained, offline-capable AI stacks for industrial settings. Based in Hsinchu County, Taiwan, he’s passionate about bridging hardware, IoT, and ML to turn lab prototypes into resilient fielded systems.
14 years of coding experience
Master of Science - MS, Electrical Engineering, Master of Science - MS, Electrical Engineering at National Sun Yat-Sen University
Bachelor of Technology - BTech, Electrical and Electronics Engineering, first, Bachelor of Technology - BTech, Electrical and Electronics Engineering, first at Bhilai Institute of Technology (BIT), Durg
English, Hindi, Chinese