Job Requirements
Design and develop AI/ML-based applications with a focus on deployment on embedded hardware platforms (e.g., Renesas RZ/V2H, NVIDIA Jetson, STM32, etc.)Port and optimize AI models for real-time performance on resource-constrained embedded systemsPerform model quantization, pruning, and conversion (e.g., ONNX, TensorRT, TVM, TFLite, DRP-AI) for deploymentEnd-to-end AI model lifecycle development including data preparation, training, validation, and inference optimizationCustomize and adapt AI network architectures for specific edge AI use cases (e.g., object detection, classification, audio detection)Data Preparation & Preprocessing: Collect, organize, and preprocess audio/image datasets.

Work Experience
Minimum 5 years of experience in AI/ML application development.Strong Python programming skills, including AI frameworks such as PyTorch, TensorFlow, Keras.Solid experience in developing deep learning-based solutions for Computer Vision, Imaging and Audio.Deep understanding of DL architectures like CNN, FCN and their application to visual tasks.Experience in model optimization techniques such as quantization, pruning, layer fusion, and INT8 calibration for edge inference.Hands-on experience in deploying AI models on embedded platforms.Proficiency in tools such as OpenCV, ONNX, TVM, TFLite, or custom inference engines.Understanding of system constraints like memory, compute, and power on edge devices.Exposure to real-time audio processing, video processing and robotics.