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ever-changing world. Key responsibilities: Designing and optimising ML models to enhance secure communications and signal processing on a range of edge devices. Implementing low-latency, high-performance deep learning pipelines on hardware accelerators such as FPGA, TPU, and ASICs. Optimising CNN, Transformer, RNN, and/… architectures for deployment on low-power embedded systems. Apply quantisation, pruning, distillation, and model compression to enhance efficiency. Strengthening model robustness against adversarial attacks and system-level security threats. Collaborating with embedded and security engineers to align AI performance with real-world system constraints. Edge AI Engineer essential experience ...