AI On-Device AI Engineer vs AI Output Filtering Engineer

A AI On-Device AI Engineer Only

• Model compression techniques: pruning, quantization-aware training, knowledge distillation, and low-rank factorization
• Edge inference frameworks: TensorFlow Lite, ONNX Runtime Mobile, Core ML, ExecuTorch, and Apache TVM
• Hardware acceleration targets: ARM NEON/SVE, Qualcomm Hexagon DSP, Apple Neural Engine, NVIDIA Jetson, Google Edge TPU
• Quantization mastery: INT8, INT4, mixed-precision, calibration datasets, and per-channel vs. per-tensor schemes
• Model conversion and graph optimization: operator fusion, constant folding, layout transformations, and custom operator authoring
• Profiling and performance analysis on real devices: latency, throughput, memory footprint, power draw, and thermal behavior
• Systems programming in C/C++/Rust for zero-copy memory management and minimal runtime overhead
• Python ML ecosystem fluency for model training, fine-tuning, and benchmarking pipelines