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AI Engineer with 1+ years in Machine Learning & Data Analysis
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Undergraduate Data Science student with practical experience in machine learning, data analysis, and software development through academic projects. Strong skills in Python, data preprocessing, model building, and analytical problem-solving, with hands-on exposure to real-world datasets. Seeking an internship or entry-level role in Data Science or AI/ML to apply data-driven insights and grow as a data professional.
SLIIT
Bachelor in Data Science · Data Science
August 1, 2024 – June 30, 2028
Royal College
High School
June 1, 2010 – May 31, 2024
X-ray ML classification system
January 1, 2026 – May 1, 2026
Developed a deep learning-based X-ray image classification system to detect normal and abnormal cases, followed by multi-class disease prediction. Implemented models such as ResNet and DenseNet using PyTorch, with data preprocessing, training, and evaluation pipelines. Integrated the ML model with a web-based application (FastAPI + React) and PostgreSQL database for real-time predictions and data management. Achieved reliable performance through model optimization and validation techniques.
Aparatment Sales Management System
June 1, 2025 – November 1, 2025
Developed a Spring Boot-based Apartment Sales Management System implementing MVC architecture, Spring Security (RBAC, BCrypt), and Spring Data JPA. Designed MySQL schemas, implemented CRUD operations for users and apartments, and built responsive dashboards using Thymeleaf and Bootstraps.
Wine Quality Prediction Machine Learning Model
June 1, 2025 – November 1, 2025
Developed a robust ML model for predicting wine quality accurately. Utilized advanced algorithms to enhance prediction precision and reliability. Built a machine learning model achieving 92% accuracy in wine quality prediction, with optimized preprocessing, feature engineering, and real-time deployment.
Online Book Store Management System
January 1, 2025 – May 1, 2025
Developed a web-based bookstore system using Java and Spring Boot with MVC architecture, supporting admin and customer roles. Implemented secure authentication, full CRUD operations for books, users, orders, and reviews, and a responsive UI using Bootstrap.
PyTorch: Fundamentals
DeepLearning.AI
January 1, 2026 – Present
PyTorch: Techniques and Ecosystem Tools
DeepLearning.AI
January 1, 2026 – Present
PyTorch for Deep Learning Professional Certificate
DeepLearning.AI
January 1, 2026 – Present
Sequences, Time Series and Prediction
DeepLearning.AI
December 1, 2025 – Present
Machine Learning
Stanford University
December 1, 2025 – Present
Supervised Machine Learning: Regression and Classification
DeepLearning.AI, Coursera, Stanford CPD, UVM
December 1, 2025 – Present
Advanced Learning Algorithms
DeepLearning.AI, Coursera, Stanford CPD, UVM
December 1, 2025 – Present
Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization
DeepLearning.AI
December 1, 2025 – Present
IBM Data Science Professional Certificate
Coursera
November 1, 2025 – Present
Cultural Fit Analysis
The candidate is an undergraduate student with a strong academic background and a clear focus on Data Science and AI/ML. The projects demonstrate initiative and a proactive approach to learning and applying technical skills. The diversity of projects (ML classification, prediction, full-stack web development) shows a broad interest and willingness to tackle different technical challenges. The numerous certifications indicate a strong drive for continuous learning and self-improvement, which aligns well with a growth-oriented culture. However, the lack of professional experience means cultural fit in a corporate environment is yet to be fully tested.
Soft Skills & Operational Fit
The candidate's project descriptions indicate an ability to work on complex, multi-faceted problems, suggesting strong problem-solving and analytical thinking. The academic nature of projects implies a structured approach to development and learning. However, without professional experience or specific team-based project details, it's difficult to assess collaborative coding, technical communication, or stress handling in a professional operational context.