Openings For Embedded AIML

Tekskills Inc.; Hyderabad; On-site; INR 1,800,000 - 3,200,000; Full time

Job summary

Tekskills Inc. is seeking an Senior Software Engineer - Embedded AI/ML in Bangalore. The role focuses on designing, developing, and implementing advanced machine learning solutions for resource-constrained devices.

Candidates should have 4+ years of experience, with 3+ years in AI/ML, and a strong background in EC/CS. Proficiency in Python, C/C++, and embedded toolchains is required, with a focus on performance and optimization.

Qualifications

• Must have M. Tech in AI/ML with EC or CS background.
• Total experience 4+ years, with at least 3+ years in AI/ML domain.

Responsibilities

• Design, develop, and implement advanced ML/DL solutions for embedded devices.
• Port AI models to MCUs, DSPs, and edge processors with optimization.
• Profile latency, throughput, memory footprint, and power consumption; optimize accordingly.

Skills

AI/ML concepts

Model training/inference/evaluation

Model optimization

Embedded AI frameworks

MCU/DSP architectures

FreeRTOS/Zephyr

Python

C/C++

Debugging/Code optimization

Git/GitHub/GitLab

Build systems (CMake/Makefiles)

Communication skills

Education

M. Tech in AI/ML with EC or CS background

Tools

TensorFlow

TFLite

TFLM

microTVM

CMSIS-NN

CMSIS-DSP

Keil MDK

IAR Embedded Workbench

VS Code

Eclipse

ST toolchains

NXP toolchains

Renesas toolchains

TI toolchains

Infineon toolchains

CMake

Makefiles

Git

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.