Openings For AIML

Tekskills Inc.; Bengaluru; On-site; INR 2,500,000 - 4,000,000; Full time

Job summary

Tekskills Inc. is hiring an Embedded AIML Engineer in Bengaluru, working in-office with 5 days of WFO. The role requires 4+ years of experience in AI/ML for embedded devices, with hands-on training, inference, and deployment on MCUs/DSPs.

You will optimize models, work with frameworks like TensorFlow/TFLite, and use IDEs such as Keil MDK, IAR, and VS Code. You should be proficient in Python and C/C++, familiar with Git workflows, and able to profile latency, memory, and power for

Qualifications

• Must have strong AI/ML fundamentals and ability to train, infer, and evaluate models on resource-constrained devices.
• Knowledge of model optimization and compression methods for embedded deployments.
• Experience with embedded AI frameworks (TensorFlow, TFLite, TFLM, microTVM, CMSIS-NN, CMSIS-DSP).
• Proficiency with IDEs like Keil MDK, IAR, VS Code, Eclipse and related toolchains.
• Solid understanding of MCU/DSP architectures, ARM Cortex-M/A, and embedded memory.
• Experience porting and deploying AI models on MCUs/DSPs/edge processors with latency/memory/power profiling.
• Strong Python and C/C++ coding and debugging skills.
• Familiarity with Git and build systems (CMake, Makefiles).
• Excellent written and verbal communication.

Skills

AI/ML concepts

Model training & inference

Model optimization & compression

TensorFlow/TFLite

microTVM/CMSIS-NN

CMSIS-DSP

MCU/DSP architectures

FreeRTOS/Zephyr

Python

C/C++

Git/GitHub/GitLab

Build systems (CMake/Makefiles)

Communication skills

Tools

Keil MDK

IAR Embedded Workbench

VS Code

Eclipse

ST/NXP/Renesas/TI/Infineon toolchains

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.