AI & Embedded Systems Engineer

Muhammad
Syahmi
Jafry

Building intelligent systems at the intersection of deep learning, microwave sensing, and embedded hardware. From RF signal pipelines to IoT architectures — engineered for the real world.

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3+ Years Building
3+ Key Projects
2× Award Winner
2 Cisco Certs

00 — About

Portrait of Muhammad Syahmi Bin Jafry


Researcher.
Builder.

I'm Muhammad Syahmi Bin Jafry, a Computer Engineering graduate from Universiti Teknikal Malaysia Melaka (UTeM) with a deep focus on AI-driven systems and embedded hardware integration.

My work spans Microwave-based wound classification using microwave sensing and deep learning, IoT livestock monitoring, and real-time signal processing pipelines — all built at the hardware-software interface where things get real.

Location Ayer Keroh, Melaka, Malaysia
Degree B.Eng. Computer Engineering
Languages English, Malay
Availability Open to Opportunities

🏆 Awards & Recognition

Jejak Inovasi UTeM 2023 — Best Project & Gold Award
INOTEK 2024 — 3rd Place, Human Interaction Category

01 — Stack

Technical
Arsenal

A diverse stack across AI, signal processing, embedded systems, and web development.

Languages

Python JavaScript C++ HTML CSS

AI & Machine Learning

Deep Learning TensorFlow Computer Vision Time-Frequency Analysis Machine Learning

Embedded & Hardware

Raspberry Pi ESP32 NanoVNA LoRa Communication IoT Architecture

Signal Processing

Microwave Signal Analysis S-Parameter Extraction RF Sensing CWT Scalograms

Tools & Platforms

MongoDB Google Colab Git & GitHub Linux Google Sheets API

Networking

Node-to-Node LoRa Gateway Integration Cloud Monitoring RFID

CCNA: Introduction to Networks

Cisco Certified Network Associate

CCNA v7: Switching, Routing & Wireless Essentials

Cisco Certified Network Associate

02 — Work

Featured
Projects

Systems built from scratch — hardware to AI pipeline.

RF wound monitoring lab setup

01 — Featured

Deep Learning Wound Monitoring & Classification System

AI-driven wound severity classification integrating NanoVNA hardware, Raspberry Pi, and microwave sensing. RF signal pipelines feed into deep learning models for healthy/mild/severe classification.

Python TensorFlow NanoVNA Raspberry Pi CWT RF Sensing
View Case Study GitHub Available on Request
Confusion matrix from wound image classification project

02

Wound Image Classification System (FYP)

Deep learning image classification system for wound analysis using TensorFlow. Automated preprocessing, dataset preparation, and model training pipeline for intelligent medical monitoring.

TensorFlow Python Computer Vision CNN
View Case Study GitHub Available on Request
DorperNet dashboard showing livestock weight tracking

03

DorperNet — IoT Smart Livestock Monitor

Automated livestock weighing and monitoring system using RFID, load cells, LoRa communication, and embedded microcontrollers with cloud-based real-time data synchronization.

ESP32 LoRa RFID IoT Cloud
View Case Study GitHub Available on Request

03 — Experience

Work History

Jun 2025 — Aug 2026

Research Assistant

Universiti Teknikal Malaysia Melaka (UTeM) — Contract

  • Developed Raspberry Pi-based systems integrated with Vector Network Analyzers for automated S-parameter extraction and real-time signal processing.
  • Designed microwave sensing experiments for wound severity classification using RF signal analysis and AI-driven processing pipelines.
  • Built hardware-software data acquisition systems integrating VNA devices, sensors, and Python-based monitoring platforms.
  • Conducted signal preprocessing and dataset preparation for deep learning model development.

Jul 2023 — Oct 2023

IoT System Developer

D'Impian Agro Farm — Internship

  • Developed an IoT-based automated livestock weighing and monitoring system using load cells, RFID tagging, LoRa communication, and microcontrollers.
  • Implemented wireless communication architecture including Node-to-Node LoRa transmission and cloud-based monitoring systems.
  • Integrated sensor data acquisition with IoT platforms and Google Sheets for centralized real-time livestock management.
  • Conducted onsite deployment, calibration, testing, and system validation in farm environments.

04 — Education

Academic
Background

2020 — 2024

Bachelor in Computer Engineering

Universiti Teknikal Malaysia Melaka (UTeM)

Completed coursework in artificial intelligence, deep learning, machine learning, IoT development, and embedded systems.

2018 — 2019

Matriculation in Science

Selangor Matriculation College — Life Science Elective

Foundation in sciences providing the analytical groundwork for engineering and research.

06 — Contact

Let's Build
Something Real

Open to research collaborations, embedded systems projects, and AI engineering roles. Based in Melaka, Malaysia — available remotely and on-site.

Email syahmijafry35@gmail.com
LinkedIn linkedin.com/in/SyahmiJafry