Openings For Embedded AIML Engineer

Tekskills Inc.; Chennai; On-site; INR 1,200,000 - 2,400,000; Full time

Job summary

You will work on DL architectures, porting models to MCUs/DSPs, optimize latency/power, and use TensorFlow/TFLite; strong Python/C++ required. Masters preferred.

Qualifications

• M.Tech in AI/ML with EC or CS.
• 4+ years total experience, 3+ years in AI/ML.

Responsibilities

• Train, infer, and evaluate AI models (supervised and unsupervised).
• Model optimization and compression techniques.
• Embedded-AI frameworks: TensorFlow, TFLite, TFLM, microTVM, CMSIS-NN, CMSIS-DSP.

Skills

DL Architectures

Python

ML Frameworks

Computer Vision

Time-Series

Video Analysis

Unsupervised Learning

Embedded AI

Education

M.Tech in AI/ML (EC/CS)

Tools

Keil MDK

IAR Embedded Workbench

VS Code

Eclipse

TensorFlow Lite

microTVM

CMSIS-NN

CMSIS-DSP

Job description

Hi

We are hiring for Embedded AIML Engineer

Experience: 4+ years

Location: Bangalore

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.

Qualifications

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.

• Core Skillset (Must-Have):
  Expertise in Deep Learning architectures: CNN, RNN, GNN, Transformers.
  Strong programming skills in Python.
• Experience with at least one major AI/ML framework (PyTorch / TensorFlow).
  Track record in Computer Vision, Time-Series Analysis, Video Analysis.
  Familiarity with Unsupervised & Semi-supervised learning techniques.
• Preferred/Good to Have:
  Image Processing, NLP, Edge/Embedded AI deployment

Roles & Responsibilities

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.

Strong verbal and written communication skills