Available from June 2026

Hi, I'm Mir Md Mohiuddin Abrar

Trainee Software Engineer Candidate

Computer Science student at BRAC University focused on Python, full-stack development, databases, software engineering, AI/ML, and LLM quantization research.

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Mir Md Mohiuddin Abrar

Software Engineering + AI/ML

Dhaka, Bangladesh

About Me

Building software with a strong CS foundation

I am a Computer Science undergraduate at BRAC University with a current CGPA of 3.55. I enjoy building practical web applications, writing clean code, and learning how modern enterprise software is designed, tested, and maintained.

My current focus areas are Python, OOP, REST-style backend logic, relational databases, software documentation, machine learning, NLP, and efficient LLM deployment through quantization.

Technical Skills

Technologies I work with

Programming

Python, C, JavaScript, SQL, OOP, Data Structures and Algorithms

Web Development

HTML, CSS, JavaScript, React, Node.js, Express.js, REST APIs

Databases

MySQL, SQL queries, relational design, SQLAlchemy, database integration

AI / ML

Machine learning, NLP, TensorFlow, neural networks, LLMs, quantization research

Selected Work

Projects

Full Stack

E-Commerce Website

CSE 470 software engineering project covering requirements analysis, authentication, dashboard features, CRUD operations, search/filter functionality, database integration, SRS, UML diagrams, and testing.

View GitHub
Software EngineeringDatabaseGit
Machine Learning

Sentiment Analysis Model Comparison

Compared Logistic Regression, Naive Bayes, and RNN with GloVe embeddings for IMDB sentiment classification. Logistic Regression with Bag-of-Words achieved approximately 88% accuracy.

PythonNLPML
View Colab
Web App

Pastport

Time capsule web application for scheduling future messages, invitations, and notes with public/private visibility and trustee-based contingency logic.

WebScheduling LogicSecurity Flow
View GitHub
Current Focus

LLM Quantization Research

I am currently researching reduced-precision quantization, model compression, inference efficiency, and deployment trade-offs for large language models. My goal is to understand how LLMs can become more practical for memory-constrained and latency-sensitive environments.

  • Model size vs. accuracy trade-offs
  • Memory-efficient inference
  • Deployment-friendly AI systems
Get in Touch

Let's connect

I am open to Trainee Software Engineer roles, internships, and junior software engineering opportunities starting from June 2026.

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