Embedded Ai Engineer

1 day ago

Avadi, India Valeo Full time

Responsibilities : Deep Learning Model Conversion: Convert and adapt deep learning network architectures (e.G., from PyTorch) for deployment on various embedded platforms. Quantization-Aware Training (QAT): Implement and fine-tune Quantization-Aware Training techniques to optimize model performance and reduce memory footprint while maintaining accuracy. Model Optimization: Perform extensive model optimization techniques, including pruning, quantization (post-training and QAT), and network architecture search, to achieve desired latency, power, and memory targets. Runtime Integration: Integrate optimized deep learning models with embedded runtime environments and hardware accelerators. Performance Profiling & Tuning: Analyze and profile model performance on target embedded hardware, identifying bottlenecks and implementing solutions for real-time inference. Number Format Conversion: Work with various number formats (e.G., FP32, FP16, INT8) and develop strategies for efficient conversion and utilization on embedded processors. Toolchain Development & Utilization: Utilize and contribute to the development of custom conversion tools and optimization scripts to streamline the deployment pipeline. Skills Required : Experience: 5+ years of experience in embedded software development with a strong focus on AI/Machine Learning deployment. Programming Skills: Proficient in Python for AI development and scripting. Deep Learning Frameworks: Hands-on experience with deep learning frameworks such as PyTorch. Experience with TensorFlow/Keras is a plus. Embedded Systems: Strong understanding of embedded system architectures, microcontrollers, DSPs, and/or FPGAs. Optimization Techniques: Proven experience with deep learning model optimization techniques (quantization, pruning, knowledge distillation). Number Formats: Familiarity with different number formats (e.G., FP32, FP16, INT8) and their implications for embedded inference. Conversion Tools: Experience with model conversion tools (e.G., ONNX, OpenVINO, TensorRT, TVM). Problem-Solving: Excellent analytical and problem-solving skills, with a strong ability to debug and optimize complex systems. Experience with C/C++ for embedded development. Familiarity with hardware acceleration (e.G., NPUs, GPUs on edge devices).

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