Openings For AIML

Tekskills Inc.; Kolkata District; On-site; INR 1,200,000 - 1,600,000; Full time

Job summary

Tekskills Inc. in Bengaluru seeks an Embedded AIML Engineer to design and deploy AI models on resource-constrained devices. The role requires 4+ years of hands-on experience with ML concepts, model training, inference, and performance profiling.

You will work with MCU architectures, ARM Cortex cores, and embedded toolchains in a 5-day WFO setup. Applicants should be proficient in Python and C/C++, familiar with Git and build systems, and capable of optimizing latency, memory footprint, and power

Qualifications

• . Must have AI/ML concepts and model training/inference experience.
• 2. Experience with model optimization and compression methods.
• 3. Experience deploying AI on MCUs/DSPs/edge devices.
• 4. Strong Python and C/C++ programming skills.
• 5. Familiar with Git and build systems (CMake/Makefiles).
• 6. Knowledge of MCU/DSP architectures and ARM Cortex cores.
• 7. RTOS knowledge (FreeRTOS/Zephyr) is a plus.
• 8. Good communication skills.

Skills

AI/ML concepts

Python

C/C++

Communication

Tools

TensorFlow

TFLite

CMSIS-NN

CMSIS-DSP

Keil MDK

IAR Embedded Workbench

VS Code

Eclipse

Git

CMake

Makefiles

Job description

Hi all

We are hiring for Embedded AIML Engineer

Work Location: Bangalore

Work mode: 5 days of WFO

Experience Range:4+ years

JD:

Embedded-AI engineer must have most of the below mentioned skillsets.

1. Must be good at AI-ML concepts. Must be able to perform model training, inference, and evaluation of different AI models (both supervised and unsupervised).
2. Different model optimization and compression methods must be known.
3. Embedded-AI frameworks like TensorFlow, TFLite, TFLM, microTVM, CMSIS-NN, CMSIS-DSP.
4. Experience of using IDEs like: Keil MDK, IAR Embedded Workbench, VS Code, Eclipse, and toolchains from ST, NXP, Renesas, TI, Infineon etc.
5. 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.
6. 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.
7. Strong proficiency in Python and C / C++ languages. Experience with software debugging and code optimization techniques.
8. Familiarity with Git/GitHub/GitLab and build systems like CMake, Makefiles.
9. Strong verbal and written communication skills.