Embedded AI Engineer
BusPatrol (company site, opens in a new tab)HybridDirect career pageEst. 18–32 LPA
Posted 18h ago
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Job description
Structured overview of role & requirements
About This Role
- Lead development and deployment of deep learning models (CNNs, YOLO, RNNs, OCR) on embedded AI hardware accelerators for real-time video analytics at the edge.
- Optimize AI models for performance, latency, and power efficiency on resource-constrained IoT and vehicle-mounted devices using accelerators like Hailo, NVIDIA Jetson, Intel Movidius, or ARM NPUs.
- Design and implement robust, fault-tolerant inference pipelines and collaborate on system architecture enabling scalable, real-time AI decision support for safety-critical environments.
Minimum Requirements
- Proficient in C/C++ and Python for embedded software and AI integration.
- Experience with model quantization, pruning, and conversion using tools such as ONNX, TFLite, TensorRT for edge deployment.
- Hands-on experience with at least one AI hardware platform (e.g., Hailo, NVIDIA Jetson, Intel Movidius, ARM Ethos-U, Google Coral).
- Bachelor’s or Master’s degree in Electrical Engineering, Computer Engineering, AI, or related technical field; Work Experience Required: Not explicitly mentioned in the JD.
Ideal Candidate Profile
- Strong expertise in embedded AI model inference pipeline development and hardware-aware optimization targeting edge computing environments.
- Experience working with Linux-based embedded systems and video/image processing frameworks like GStreamer and OpenCV in real-world mobile or vehicle contexts.
- Ability to lead cloud-to-edge AI transformation efforts and ensure fault-tolerant, low-latency AI deployment in safety-critical applications.