Embedded AI Engineer

1. AI Model Optimization & Deployment:

• Analyze trained AI models (from TensorFlow, PyTorch) to identify performance bottlenecks and resource requirements (memory, compute).
• Convert models into inference-optimized formats such as ONNX, TensorRT, and TFLite.
• Apply advanced optimization techniques, including quantization (INT8/FP16) and pruning, to reduce model size and accelerate processing speed while maintaining accuracy.
• Profile and benchmark model performance on target hardware (e.g., NVIDIA Jetson, ARM CPUs) to ensure latency and throughput criteria are met.

2. Application Software Development:

• Build high-performance applications and libraries in C++/Python to load, manage, and execute AI models in both Linux and Windows environments.
• Develop end-to-end data processing pipelines, from pre-processing input data (images, video) to post-processing model outputs.
• Create and maintain unit and integration tests to ensure the stability and accuracy of AI features.

3. Research & Improvement:

• Stay current with the latest technologies, algorithms, and tools in embedded AI and efficient machine learning.
• Research and experiment with new AI models to evaluate their feasibility and potential for product application.
• Participate in troubleshooting, debugging, and continuously improving deployed AI systems to enhance performance and reliability.

4. Collaboration & Technical Support:

• Collaborate closely with AI/ML scientists to understand model architectures and deployment requirements.
• Work with hardware engineering teams to leverage specialized on-chip acceleration features.
• Support other teams (such as QA and Product) in integrating and testing AI solutions.