Embedded-AI engineer must have most of the below mentioned skillsets. Must be valuable at AI-ML concepts.
Responsibilities
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.
Strong 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.
Robust verbal and written communication skills.
Key Skills
Python
C
C++
TensorFlow
TFLite
CMSIS-NN
Cmsis-dsp
FreeRTOS
GitHub.
📌 Aiml Engineer Bengaluru
🏢 Tekskills
📍 Bengaluru
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