Hi, my name is
Ehsanur Rahman Rhythm.
I build things for the web.
I'm a software engineer specializing in startup development and creating dynamic, user-friendly web applications. Currently, I'm enhancing the returns management experience at iF returns while revolutionizing emergency delivery systems at DeliveryHobe.
About Me
Hello! My name is Ehsanur Rahman Rhythm, and I enjoy creating things that live on the internet. My journey in web development started during my time at Brac University, where I developed a passion for building user-friendly and dynamic web applications.
Fast-forward to today, I have over four years of experience working with various technologies, including PHP, Laravel, and WordPress. I’ve had the privilege of working on a range of projects, from developing a digital database management system for the Marine Fisheries Academy to creating high-traffic educational websites and news portals.
Currently, I'm working at iF returns 🇪🇸 and DeliveryHobe 🇧🇩 . My main goal is to build accessible, human-centered products that meet and exceed user expectations.
In addition to my work in web development, I have also published research in the fields of natural language processing (NLP) and data science. I worked as a Research Assistant at Brac University, researching machine learning-based accessibility systems for visually impaired individuals on campus. These publications reflect my commitment to advancing technology and contributing to the academic community.
Here are a few technologies I've been working with recently:
- React.js
- Express.js
- Astro.js
- Vanilla JavaScript
- PHP
- Laravel
- WordPress
Where I’ve Worked
Software Engineer @ iF returns
Jun 2025 - Present
- Improved customer return process dashboard by implementing intuitive features and optimizing performance for seamless user experience
- Maintained serverless backend infrastructure using AWS services (Lambda, API Gateway) to support scalable operations
- Led cross-functional collaboration to streamline return workflows through detailed analysis and documentation, driving 30% reduction in processing time
Where I’ve Studied
Some Things I've Built
Featured Project
From Your Desktop to Your Doorstep: Explore DeliveryHobe's New Website for Local Deliveries!
Guess what? DeliveryHobe is now on your desktop! We're thrilled to introduce our redesigned website, fully optimized for desktop users. This means you can shop from your favorite local stores, get anything delivered in 30 minutes, and support your community, all from the comfort of your computer. With this update, you get a seamless experience on a larger screen, making it easier to browse products, navigate, and order. Whether you're at home, work, or anywhere else, DeliveryHobe is just a click away, ensuring you can always support local stores with ease.
Tech Stack:- Next.js
- React
- TailwindCSS
- NestJS
- ShadCN
Featured Project
Facultypedia: A Faculty Review and Consultation Platform
FacultyPedia is a comprehensive faculty review and consultation platform designed to empower students to make informed decisions about their education. The platform aims to provide a transparent and reliable source of information for students seeking guidance on faculty members, courses, and academic institutions.
Tech Stack:- Laravel
- PHP
- TailwindCSS
- MySQL
- DaisyUI
Featured Project
Pathways of Learning: A Website Redesign for Dr. Santosh Sali
Pathways of Learning is a blog created by Dr. Santosh Sali, an educator and organizational behavior enthusiast. The blog focuses on his insights into business planning, teaching, and his passion for books, including book reviews.
Tech Stack:- WordPress
- PHP
- CSS
- Elementor
Other Noteworthy Projects
view the archiveFrom K-Drama to Code: My Journey Building The Pyramid Game
You know how sometimes you're just chilling, watching K-dramas, and suddenly your developer brain goes "Hey, I could build that!"? Well, that's exactly what ...
How I Implemented the Similar Petitions Suggestions in Jonogon
Imagine pouring your heart into drafting a petition for your country, only to find that someone else has already raised the same issue. It was clear: duplica...
Berj & Emilia Hovsepian: A Custom Wedding Website Design
Berj Hovsepian wanted a wedding website that captured the elegance of a traditional wedding card, where guests could easily find event details and RSVP. The ...
Moz Education - Education Consultancy Platform
Moz Education approached me with a vision: to create an intuitive and visually appealing website that would serve as the cornerstone of their online learning...
Publications
view all publications| Title | Venue | Year | |
|---|---|---|---|
Text-based Q&A: Automated Question Generation and Answering for Enhanced Data Processing In the age of information overload, where the world is getting increasingly digital, traditional methods of learning are becoming tedious and extensively outdated. Imagine a system with just a few clicks that can quickly generate perplexing questions and enlightening solutions from a given text. Likewise, this paper represents a groundbreaking system that uses stateof-the-art of natural language processing techniques to analyze subject-specific chapters to create questions and corresponding solutions of varying lengths. The system’s versatility as an ideal tool for a wide range of users, including students, researchers, and educators, is a result of its capability to handle a wide range of domains. By offering questions of insight along with appropriate answers, this aforementioned structure demonstrated its extraordinary accuracy and competency in our studies on an array of informational datasets. Altering the learning process and promoting knowledge discovery, this program is flexible in delivering brief or comprehensible solutions to inquiries, having the potential to completely change how individuals interact with written material, whether they are reading for short reference or conducting any depth research. Our suggested framework establishes a dynamic platform for immediate information that enables people to learn substantially more and comprehend any topic in depth. With this leading-edge method, bid goodbye to exhausting manual question generation and get ready to embrace a new era of seamless and fruitful learning with this cutting-edge system. | CATS | 2023 | |
Advancements in Optical Character Recognition for Bangla Scripts Optical Character Recognition (OCR) systems are very powerful tools that are used to convert handwritten texts or digital data on an image to machine readable texts. The importance of Optical Character Recognition for handwritten documents cannot be overstated due to its widespread use in human transactions. OCR technology allows for the conversion of various types of documents or images into machine understandable data that can be analyzed, edited, and searched. In earlier years, manually crafted feature extraction techniques were used on comparatively small datasets which were not good enough for practical use. With the advent of deep learning, it was possible to perform OCR tasks more efficiently and accurately than ever before. In this paper, several OCR techniques have been reviewed. We mostly reviewed works on Bangla scripts and also gave an overview of the contemporary works and recent progresses in OCR technology (e.g. TrOCR, transformer w/CNN). It was found that for Bangla handwritten texts, CNN models like DenseNet121, ResNet50, MobileNet etc are the commonly adopted techniques because of their state of the art performance in object recognition tasks. Using an RNN layer like LSTM or GRU alongside the base CNN-based architecture, the accuracy can be further improved. TrOCR is a fairly new technique in this field that shows promise. Experimental results show that in synthetic IAM handwriting dataset it showed a Character Error Rate (CER) of 2.89. The goal of this paper is to provide a summary of the research conducted on character recognition of handwritten documents in Bangla Scripts and suggest future research directions. | ASYU | 2023 | |
Automatic Subtitle Generation for Bengali Multimedia using Deep Learning For audio or video material to be more inclusive and accessible, automatic subtitle generation is essential. Nevertheless, implementing this technology into Bengali presents significant challenges due to scarce resources and linguistic difficulty. In this study, a new deep learning based system for creating Subtitles for Bengali multimedia automatically is introduced. The suggested approach makes use of the Wav2vec2 and the Common Voice Bengali Dataset, a large collection of Bengali audio recordings. This study uses the Common Voice Dataset Bengali to train and tune the Wav2vec2 model in order to accurately convert Bengali audio into text. Current automatic speech recognition approaches are combined with Bengali language-specific factors in the created system to give accurate and reliable transcription works. The transcribed text is synced with the matching audio parts throughout the subtitle production process. The produced subtitles are enhanced using post-processing approaches, similar to capitalization and punctuation restoration, to ensure readability and consistency. The findings of this study might greatly improve Bengali language media’s usability and availability across a range of sectors. The created subtitles may enhance the watching experience for Bengali multimedia by easing greater understanding, and expanding availability. The study demonstrates the potential of using deep learning and ASR methods to get over the difficulties of automated subtitle production in the Bengali language, advancing multimedia availability and inclusion. | B.Sc, Brac University | 2023 | |
Sentiment Analysis of Restaurant Reviews from Bangladeshi Food Delivery Apps In this study, we conducted sentiment analysis on restaurant reviews from Bangladeshi food delivery apps using natural language processing techniques. Food delivery apps have become increasingly popular in Bangladesh, and understanding the sentiment of customer reviews can provide valuable insights for restaurant owners and food delivery app companies. In this research, we have created a dataset named “Bangladeshi Restaurant Reviews” by gathering customer reviews of restau-rants available on Foodpanda and Hungrynaki, which are two popular food delivery apps in Bangladesh. We used Robustly Optimized BERT Pretraining Approach (RoBERTa), AFINN, and DistilBERT, a distilled version of Bidirectional Encoder Repre-sentations from Transformers (BERT) to perform the sentiment analysis. Overall, this research paper highlights the importance of sentiment analysis in the food delivery industry and demonstrates the effectiveness of different models in performing this task. It also provides insights for businesses looking to use sentiment analysis to improve their services and products. The accuracy of the models evaluated, RoBERTa, AFINN, and DistilBERT, were 74%, 73%, and 77% respectively. | ESCI | 2023 |
What’s Next?
Get In Touch
Although I’m not currently looking for any new opportunities, my inbox is always open. Whether you have a question or just want to say hi, I’ll try my best to get back to you!
Say Hello