Openings For Embedded AIML

Tekskills Inc.; Bengaluru; On-site; INR 1,800,000 - 2,800,000; Full time

Job summary

Tekskills Inc. in Bengaluru is seeking a Senior Software Engineer with a focus on embedded AI/ML to design, develop, and implement advanced machine learning solutions for resource-constrained devices.

You will work on state-of-the-art architectures, model optimization, and deployment on MCUs and edge processors, ensuring low latency and power efficiency. 4+ years of experience required, Bengaluru-based with 5 days a week in the office.

Qualifications

• M.Tech in AI/ML with background in Electronics & CS/EC.
• 4+ years total experience, 3+ years in AI/ML domain.
• Strong programming skills in Python and C/C++.

Responsibilities

• Design and develop embedded AI/ML solutions.
• Port and deploy AI models on MCUs/edge devices.
• Optimize latency, throughput, memory, and power consumption.
• Collaborate with hardware and software teams.

Skills

AI/ML concepts

Model training & evaluation

Model optimization & compression

Python

C/C++

Embedded systems

Version control (Git)

Communication skills

Education

M.Tech in AI/ML with EC or CS

Tools

TensorFlow

TFLite

TFLM

microTVM

CMSIS-NN

CMSIS-DSP

Keil MDK

IAR Embedded Workbench

VS Code

Eclipse

Embedded toolchains (ST, NXP, Renesas, TI, Infineon)

CMake

Makefiles

FreeRTOS

Zephyr

Job description

Hi

We are Embedded AIML Engineer

Work Location: Bangalore

Work mode: 5 days of WFO

Experience Range:4+ years

Mandatory:

• M. Tech in AI/ML with background in Electronics & Communication (EC) or Computer Science (CS).
• Experience: 4+ years total, with at least 3+ years relevant in AI/ML domain.

JD:

Position: Senior Software Engineer
We are seeking an experienced AI/ML Engineer with a strong academic and research foundation to contribute to the design, development, and implementation of advanced machine learning and deep learning solutions. The role requires expertise in state-of-the-art architectures, model optimization, and problem-solving applied to real-world scenarios.

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.