We are seeking an experienced AI Enablement Engineer to work on AI acceleration, kernel optimization, and deployment of machine learning workloads on embedded and edge platforms. The ideal candidate will have hands-on experience with AI accelerator toolchains, embedded software environments, and performance optimization for NPUs, DSPs, and GPUs.

Responsibilities :

– Enable and optimize AI/ML workloads on embedded and edge platforms

– Integrate and deploy AI models across accelerator hardware and embedded systems

– Perform kernel-level optimization and performance tuning for inference workloads

– Optimize AI pipelines for latency, throughput, memory, and power efficiency

– Implement and evaluate quantization techniques for efficient inference

– Develop benchmarking, debugging, and automation tools

– Collaborate with hardware, firmware, and AI model teams for end-to-end enablement

– Support AI deployment on embedded Linux, RTOS, and bare-metal platforms

Skills & Requirements :

– Hands-on experience with AI accelerator toolchains such as Qualcomm AI Engine, TI C7x/TDA4x, NVIDIA GPUs, MediaTek APU, or similar platforms

– Strong programming skills in Python and C/C++ for low-level and kernel-level optimization

– Working knowledge of ONNX, TensorFlow Lite (TFLite), and PyTorch model formats

– Understanding of quantization techniques including INT8, FP16, and mixed precision

– Familiarity with embedded Linux, RTOS, and bare-metal environments

– Experience with AI inference optimization, profiling, and performance tuning

– Exposure to NPU/DSP accelerator integration and kernel optimization

– Knowledge of memory optimization and heterogeneous compute architectures

– Strong debugging and problem-solving skills

– Good verbal and written communication skills

Are you interested in this position?

Apply by clicking on the “Apply Now” button below!

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