Hamdi Yaser Farid

💻 Full Stack Developer | 🧠 AI/ML Enthusiast

👨🏽‍💼 About Me

👋 Hi there! I'm Hamdi Farid, a passionate developer with a strong focus on creating innovative solutions using a diverse set of technologies. I specialize in building robust, scalable applications and AI/ML models.

🔧 Technologies Used

🧑🏽‍💻 Experience

Hardware/Software Engineer (Research and Development Branch)

Xclency Controls | Thane, Maharashtra

2022-Present

Software Developer

Xclency Technology L.L.P | Thane, Maharashtra

2020-2022

👨🏽‍🎓 Education

👨🏽‍🔬 Skills

Adaptability:

Problem-solving:

Creativity:

Collaboration:

Communication:

Time Management:

Attention to Detail:

Continuous Learning:

Empathy:

Resilience:

🦺 Projects

Schneider Electric EWS Smart Connector Integration

In this project, I was tasked to design and develop C# DLL files for EWS based software in Schneider Electric automation systems. The task required managing source code for different types of correlated projects like SNMP communication nodes, SQL data storage and retrieval for sensors using ADO.NET, and web apps that provided a user interface for the engineer/technician to manage I/Os of the network. I was solely responsible for designing, testing, and developing the architecture of the system to be deployed in foreign gulf countries.

App Development (IONIC) Greenie-Energy

This project had a span of 4 months where I needed to design an app that would be used to locate charging stations owned by the company. The app also had features such as Razor payment integration, charging initiation, exemption, and recharge. I also designed and developed new APIs which communicated using REST methods with the application for registering and logging in users. The app is currently available online on the App Store as well as the Play Store. However, my terms of contract ended in 2022.

Web App Development using Node.js MVC Architecture

This project required designing a web app for employees in the company to manage their data. Due to low relational conditions prompted during the tasks, I shifted my database from SQL to MongoDB as it provided faster performance without the cost of relations. I used Express for my Node.js server and basic REST API methods with a few exceptions of SOAP APIs.

KIOSK Based Touchscreen App for Linux Systems using Electron.js

This project was in correlation to the Schneider Electric systems where I had to design a full app with features to control smart home appliances. Initially, I worked on the front end and back end of the application using Electron.js and Node.js. Due to my experience and education in electronics, I shifted my backend to Python to efficiently code and communicate with smart home systems. I mostly worked with communication protocols like MODBUS and SNMP during this project and used SQL databases for data storage.

App Development Electric Vehicle Charging in Flutter (In Development)

This recent project, currently on hold due to backend complications, has the front end of the app ready and awaiting further development. I was tasked with translating UI/UX design to Flutter-based widgets and handling state management using Riverpod dependency, deciding between stateless and stateful widgets for screens and controls. I was responsible for handling the logic of the app, registration using the phone number, and QR scanning with automatic light state. I also used Stream and Future builders for WebSocket and Web API consumption.

Machine Learning Image Classification

Multiple and Binary Image Classification using TensorFlow (Research and Development): This ongoing project involves creating a model that can detect custom images based on the company's needs. Currently, I am researching optimizing my trained .h5 models or .keras models and using them in other programming languages like Dart and Node.js. In the future, I plan to use these models for more complex image classification by increasing variables in dense layers. For now, I am using basic training and validation dataset images to identify efficient model layers for high accurate training, reducing underfitting, and overfitting conditions.