Openings For AIML

Tekskills Inc.; Mumbai; On-site; INR 1,800,000 - 3,400,000; Full time

Job summary

Tekskills Inc. is seeking an Embedded AIML Engineer to join our team in Bangalore. The role requires 4+ years of experience in AI/ML concepts, model training, inference, and deployment on resource-constrained embedded devices.

You will optimize models for MCUs/DSPs, work with frameworks like TensorFlow and TFLite, and use IDEs such as Keil MDK and IAR. Strong Python/C++ skills and Git workflow are essential. The position follows a 5-day work week with on-site work in Bangalore.

Qualifications

• Must be good at AI-ML concepts and able to perform model training, inference, and evaluation of different AI models.
• Familiar with model optimization and compression methods.
• Experience with embedded AI frameworks like TensorFlow, TFLite, TFLM, microTVM, CMSIS-NN, CMSIS-DSP.
• Experience using IDEs such as Keil MDK, IAR Embedded Workbench, VS Code, Eclipse and toolchains from ST, NXP, Renesas, TI, Infineon.
• Strong understanding of MCU/DSP architectures, ARM Cortex-M/A, embedded memory architectures; exposure to FreeRTOS/Zephyr is a plus.
• Experience porting/deploying AI models on resource-constrained devices and profiling for latency, throughput, memory, and power.
• Strong Python and C/C++ skills; debugging and code optimization.
• Familiarity with Git/GitHub/GitLab and build systems like CMake/Makefiles.
• Strong verbal and written communication skills.

Skills

AI/ML concepts

Model training

Model inference

Model evaluation

Optimization methods

Model compression

Python

C/C++

Git/GitHub/GitLab

Profiling/Optimization

Edge deployment

ARM Cortex-M/A

FreeRTOS/Zephyr

Tools

TensorFlow

TFLite

TFLM

microTVM

CMSIS-NN

CMSIS-DSP

Keil MDK

IAR Embedded Workbench

VS Code

Eclipse

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.