Senior Edge AI Engineer

About the job

Job Requirements About the role: Experienced Edge AI Engineer to design, optimize and deploy computer-vision and real-time ML applications on resource-constrained embedded hardware (Renesas RZ/V series, RA/RA8P1, Jetson, STM32, NXP/Infineon MCUs, etc). The role owns the end-to-end AI lifecycle on board (model -> optimization -> deployment -> embedded pipeline) and is responsible for production-grade integration, profiling and delivery of model artifacts and embedded software for field devices. Primary responsibilities: • Design and develop AI/ML applications for edge devices with a strong focus on embedded deployment (RZ/V2H, RA8P1, RA6, STM32, Jetson, etc.). • Port and optimize neural networks for real-time inference on constrained MCUs/MPUs and NPUs: conversion, quantization (PTQ/QAT), pruning, layer fusion and operator mapping. • Convert and prepare models for embedded runtimes (ONNX, TFLite, TorchScript, TVM/DRP-AI, TensorRT) and integrate with runtime stacks (ONNX Runtime, TFLite-Micro, custom inference engines). • Develop and maintain C/C++ embedded application layers that host the inference engine and handle sensor I/O, DMA, framebuffers, and real-time buffering. • Implement and run benchmarking, profiling, and verification tests (latency, throughput, memory footprint, power) across multiple platforms and configurations. • Design pre & post-processing logic for vision outputs and integrate into application stack. • Produce reproducible artifacts: e2s / map files, model cards, benchmark reports, code stubs and deployment guides.

Requirements

• AI/ML Applications
• C/C++
• Embedded Systems
• Computer Vision
• Benchmarking

Preferred Technologies

• AI/ML Applications
• C/C++
• Embedded Systems
• Computer Vision
• Benchmarking