Must be valuable at AI-ML concepts. Must be able to perform model training, inference, and evaluation of different AI models (both supervised and unsupervised).
Different model optimization and compression methods must be known.
Embedded-AI frameworks like TensorFlow, TFLite, TFLM, microTVM, CMSIS-NN, CMSIS-DSP.
Experience of using IDEs like: Keil MDK, IAR Embedded Workbench, VS Code, Eclipse, and toolchains from ST, NXP, Renesas, TI, Infineon etc.
Robust understanding of MCU and DSP architectures. Knowledge of ARM Cortex-M/A processors and embedded memory architectures.

Experience with: FreeRTOS and Zephyr is an added advantage.
Experience in porting and deploying AI models on resource-constrained devices such as MCUs, DSPs, and edge processors. Performance profiling and optimization of Latency, Throughput, Memory footprint, and Power consumption.
Solid proficiency in Python and C / C++ languages.

Experience with software debugging and code optimization techniques.
Familiarity with Git/GitHub/GitLab and build systems like CMake, Makefiles.
Strong verbal and written communication skills

📌 Aiml Bengaluru (India)
🏢 Tekskills
📍 India