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Aizzat R.

Big Data · Mid-level

Available profile

Professional Overview

I am a self-taught programmer and developer with about 7 years professional experience in system integration, data collection and analysis, and fullstack development.Currently, I am an AI Engineer who focuses on image detection and classification from large scale unmanned aerial vehicles (UAV) data capture. Besides that, I also developed analytics and process automation for the captured data. I have extensive work experience regarding system integration using LabVIEW, Python, and C#. Most of the integration was using TCP/IP as the communication method between different components such as sensors, actuators, and backend databases on PostgreSQL. Some of the projects includes Automated Traffic Information System (ATIS), Weigh-in-motion (WIM) system, Drive-thru Access Control (DTAC) system, and people monitoring system. I have also worked on data analysis project such as using Computer vision for object counting. I generally used image detection and classification using machine learning algorithm in Python. One of the projects was on vehicle counting on highways to analyse and calculate frequency, type, speed, and size.Other than that, I have worked as a senior software engineer on fullstack development with UI integration mainly using Unity with C# and some PHP for connection with backend database on MySQL. The project was of a general pathfinding solution for malls. The solution uses simple AI navigation to connect and show possible routes to retail outlets or other destinations. It is also used to show events and promotions for the mall.I also have about 4 years of personal experience in developing games using Unity and C#. I have released a couple of games on the Google Play Store. My games can be found on: pl41d.itch.io

Areas of Expertise

computer vision

Develop image recognition, object detection, video analysis, and multimodal AI systems for real-world applications.

AI & Machine Learning Engineers

Design, train, and ship production ML models — from data pipelines to model inference at scale. Covers supervised learning, neural networks, LLM integration, and model optimisation.

data science & analytics

Extract business insights from raw data using statistical modelling, predictive analytics, A/B testing, and interactive dashboards.

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