Embedded ai engineer

Responsibilities :

Deep Learning Model Conversion : Convert and adapt deep learning network architectures (e.g., from Py Torch) 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 Py Torch. Experience with Tensor Flow / 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, Open VINO, Tensor RT, 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).