Building reliable, AI-augmented embedded systems for real-time monitoring, predictive analytics, and industrial automation. 10+ years bridging firmware development with scalable cloud solutions.
Software and Embedded Systems Engineer with 10+ years of experience specializing in building AI-augmented embedded platforms for real-time monitoring, predictive analytics, and automation across domains such as industrial IoT, structural health monitoring, and energy systems.
I combine deep technical expertise in firmware development (STM32, ESP32, Raspberry Pi) with modern cloud technologies, creating scalable solutions that bridge the gap between hardware and software ecosystems. My work spans the full stack: from sensor signal conditioning and MCU firmware to cloud ingestion pipelines, ML-based analytics, and mobile apps.
Personal R&D venture developing earthquake sensing and building health monitoring prototypes. Currently validating a system architecture featuring ESP32/STM32 + ADXL355 accelerometers, environmental sensors, and network connectivity for event detection and early warning.
Led R&D team shipping reverse vending machines (RVM) with barcode readers, camera + GPU inference, weight sensors, and mechatronic modules. Architected end-to-end integration from embedded edge PC to cloud fleet management.
Led development of wind turbine blade monitoring devices. Built solar-powered, rugged sensor nodes with ESP32 + 4 I2S microphones, environmental sensors, and accelerometer for vibration analysis. Data uplinked via GSM/LTE to AWS cloud for ML-based fault detection.
Delivered project-based Python and algorithms instruction. Guided learners through building ML/web applications with Flask and Django, emphasizing clean code practices and version control.
Architected and deployed earthquake early-warning systems across 50+ stations in Izmir. Device architecture: STM32 MCU + ADXL355 accelerometer → SPI → Raspberry Pi gateway with touch-screen UI.
Delivered 100+ client projects across IoT/embedded, machine learning, computer vision, and full-stack backend systems. Strong delivery metrics on freelance platforms with high repeat-client rate.
Laboratory/thesis-focused R&D building field-deployed sensor systems with end-to-end data collection, processing, and visualization. Projects: structural health monitoring (frequency generator), smart classroom, multi-modal sensor networks.
Deep dives into real engineering challenges I've solved
Izmir needed a city-wide earthquake early warning system that could detect seismic events and trigger automated safety responses within seconds.
STM32 MCU + ADXL355 accelerometer → SPI → Raspberry Pi gateway → MQTT → Node.js ingestion → RabbitMQ → Python processing → PostgreSQL.
Deploy and operate a fleet of RVMs for recyclable container collection with accurate AI-based container classification on edge hardware.
Barcode reader + camera + weight sensors → Edge PC with GPU (YOLO inference) → C++ orchestration → Node.js cloud ingestion → Kubernetes on Azure.
Wind farm operators needed to detect blade damage early through audio and vibration analysis, avoiding expensive manual inspections.
ESP32 + 4x I2S microphones + accelerometer + environmental sensors → FreeRTOS firmware → SD buffer + GSM/LTE uplink → AWS IoT Core → ML pipeline.
Research project to monitor and optimize classroom environments for student comfort and energy efficiency.
Arduino sensor nodes (CO2, light, sound, temp/humidity) → HC-12 wireless → Raspberry Pi aggregator → Python backend → PostgreSQL → Web dashboard.
Technologies I use daily to build reliable systems
Ege University, Izmir, Turkey
Thesis: A Big Data and AI–Driven Embedded Systems Framework for Structural Health Monitoring, Lifecycle Extension, and Predictive Maintenance
Ege University, Izmir, Turkey
Middle East Technical University, Ankara, Turkey
I'm always open to discussing new opportunities, innovative projects, and potential collaborations in embedded systems, IoT, and AI-driven solutions.