Work mode: 5 days of WFO

Experience Range: 4+ years

Job Summary

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.
- Strong 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.
- Strong verbal and written communication skills.

Key Skills

- Python
- C
- C++
- TensorFlow
- TFLite
- CMSIS-NN
- Cmsis-dsp
- FreeRTOS
- GitHub.

📌 AIML Engineer (Bengaluru)
🏢 Tekskills
📍 Bengaluru