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MD ARIF KHAN

Software Engineer

My Story

The journey has never been limited to classrooms, assignments, and exams. During my university years, I tried to make the most of the opportunities around me. I was involved in university clubs and volunteered in different activities, where I learned to work with people, take responsibility, and understand the value of teamwork.

As a Computer Science student, I always dreamed of working at a leading software company. With that goal in mind, I started preparing myself early, improving my technical skills and learning beyond my academic coursework.

In early 2025, I applied for the Star Coder program at Brain Station 23. I made it through the first round but couldn't move beyond the second. Although it was disappointing, the experience helped me understand where I needed to improve. Instead of giving up, I continued learning and preparing for the next opportunity

Professional Experience

2026 - Present

Enterprise Solutions Trainee

Baridhara,Dhaka
  • Gained hands on experience in enterprise solution development, focusing on business process automation, system configuration, and application development.
  • Applied requirement analysis, data modeling, software engineering principles to implement business solutions.
  • Learned project management practices, including task planning, and issue management using Jira and Redmine.
2025 - 2026

Executive Mobile App

Gulshan,Dhaka
  • Take ownership of end to end feature development from requirement analysis.
  • Collaborate with cross functional teams and stakeholders to ensure timely project delivery and product quality.
  • implement scalable application architecture following Clean Architecture principles.

Education

B.Sc. in Computer Science and Engineering (CSE)

2025

Technical Skills

OOPJavaScriptAPEXABAPSOQLSQLVersion Control (Git)Docker

Software

SalesforceSAPMicrosoft Excel

Project Management

JiraRedmine

Academic Thesis

Title: Intelligent Lost & Found Platform using Machine Learning for Automated Item Matching

Developed an intelligent Lost & Found platform that automates item matching using computer vision and natural language processing. The system integrates DINOv2 and SIFT for visual feature extraction, BGE-Reranker for multilingual semantic text matching, and XGBoost for ensemble-based match prediction. Built with a FastAPI backend, the platform supports English, Bangla, and Romanized Bangla descriptions. The proposed model achieved 80.98% accuracy, demonstrating its effectiveness in matching lost and found items. .

Title: Intelligent Lost & Found Platform using Machine Learning for Automated Item Matching cover

Let's Work Together

Have a project in mind or want to discuss job opportunities? Feel free to reach out directly.