Senior Software Engineer – EDGE AI Development

Key Responsibilities

Design and Development:

• Design and implement AI/ML-based applications tailored for embedded and edge hardware.
• Develop end-to-end pipelines for model training, conversion, and deployment.
• Customize neural network architectures for edge-specific applications such as object detection, classification, and segmentation.

Model Optimization and Deployment:

• Port and optimize AI models to meet performance and memory constraints on edge platforms.
• Apply quantization (e.g., INT8), pruning, and layer fusion techniques to improve model efficiency.
• Convert models between various formats such as ONNX, TensorRT, TVM, TFLite, and DRP-AI.

Performance Tuning and Profiling:

• Analyze model bottlenecks and tune for latency, throughput, and power efficiency.
• Run inference performance profiling on hardware targets and iterate for improvements.

Testing and Debugging:

• Validate model accuracy and performance post-optimization.
• Debug and troubleshoot model behavior discrepancies across frameworks and devices.

Documentation and Research:

• Maintain documentation for all model lifecycle stages and optimization steps.
• Stay updated with latest AI compiler advancements and deployment trends in edge AI.

Must Have:

• Bachelor's/Master’s degree in Computer Science, Electronics, or AI-related field.
• 6+ years in AI/ML model development with experience in real-world applications.
• Proficient in Python, C++ and deep learning libraries (TensorFlow, PyTorch, Keras).
• Solid understanding of CNNs, FCNs, and their applications in computer vision.
• Practical knowledge of model optimization workflows (quantization, pruning, etc.).
• Experience with ONNX, TVM, TensorRT, DRP-AI, TFLite, OpenCV, etc.
• Experience with deployment on edge devices like Jetson, RZ/V2H, or STM32.
• Strong understanding of constraints (compute, memory, power) in edge environments.

Good to Have:

• Exposure to embedded Linux or RTOS environments.
• Familiarity with low-level model debugging, calibration tools, and inference engines.
• Experience with Continuous Integration tools such as Git, Jenkins, or similar.
• Understanding of hardware accelerators (GPU, NPU, DRP-AI, etc.).