AI/ML Engineer & Full-Stack Developer

Vimansha
Jayarathna

BSc(Hons.) Statistics & Operations Research · Second Class Upper

Building intelligent systems at the intersection of data science and modern web engineering — from LLM-powered features to production-ready full-stack applications.

View My Work Get In Touch Download CV
4+
ML Projects
2
Internships
6+
Certifications
2+
Hackathons
Vimansha Jayarathna
Open to WorkAI/ML · Full-Stack
01

About Me

I'm a recent graduate passionate about blending statistical rigour with cutting-edge engineering. My degree in Statistics & Operations Research gave me a deep foundation in data — and my hands-on experience in AI/ML and full-stack development taught me how to deploy that knowledge into products people actually use.

Currently an Intern AI/ML Engineer at Insharp Technologies, I'm shaping an AI-powered women's clothing platform — from virtual fit-on rooms to LLM-driven styling recommendations — while handling the entire frontend in Next.js.

I'm driven by creating scalable, user-centric systems that merge machine learning, optimization, and elegant UI design.

Machine Learning LLM Integration Full-Stack Dev Statistical Analysis Deep Learning Data Visualization
Degree
BSc(Hons.) Statistics & Operations Research
University
Faculty of Science, University of Peradeniya
Classification
Second Class Upper Division · 2021–2025
Location
Maharagama, Sri Lanka
Languages
English · Sinhala · Tamil
Award
🏆 Rising Star Award — STEMUP Educational Foundation, 2025
02

Work Experience

Oct 2025 – Present
Intern AI/ML Engineer
Insharp Technologies
  • Designed AI-powered features for a women's clothing platform — virtual fit-on room, measurement suggestions, body-type outfit recommendations, and intelligent outfit pairing.
  • Integrated LLM-powered services (Gemini-based APIs) for personalized styling insights and enhanced user experience.
  • Developed the full frontend in Next.js & Tailwind CSS, with complete backend integration for client and admin portals.
  • Collaborated on model fine-tuning and API orchestration using Python, FastAPI, PostgreSQL, Swagger, and Postman.
  • Deployed ML components (including linear regression) within the production pipeline using Git & GitHub.
Python FastAPI Next.js Tailwind CSS PostgreSQL Gemini API Linear Regression
Sep 2024 – Mar 2025
Intern Web Developer
Department of Archaeology — University of Peradeniya
  • Developed the official responsive website for the Department of Archaeology using PHP and React.
  • Utilised Git and GitHub for version control and team collaboration across the development cycle.
PHP React Git GitHub
03

Skills & Technologies

Programming Languages
PythonJava
AI & Machine Learning
TensorFlowKerasScikit-learnNumPyPandasOpenCVMediaPipe
Neural Networks & Deep Learning
NLPCNNRNNLSTM
Web Technologies
ReactNext.jsFlaskFastAPIJavaScriptTypeScriptPHPTailwind
Databases
MySQLMongoDBPostgreSQLSnowflake
Data Analysis & Visualization
ExcelPower BITableauMatplotlib
Dev Tools & Version Control
GitGitHubDockerVS CodePyCharmPostmanSwagger
Soft Skills
Fast LearningCommunicationTeamworkAnalytical ThinkingProblem Solving
04

Featured Projects

// 01
Demo GitHub
Perceptron Classifier: Interactive ML Visualization App

An interactive web app that visualises ML concepts with real-time decision boundary updates. Built a custom NumPy perceptron backend with Flask and dynamic Plotly.js frontend — letting users experiment and learn by doing.

Python Flask NumPy JavaScript Plotly.js
// 02
Demo GitHub
AI-Powered Tweet Classification

Deep learning web interface to classify tweets as personal vs. non-personal health mentions using LSTM and Bi-LSTM neural networks, achieving over 80% classification accuracy.

Python TensorFlow Keras LSTM Gradio Scikit-learn
// 01
Demo GitHub
Laptop Price Predictor

ML-powered web application that predicts laptop prices based on user inputs (RAM, brand, OS, CPU) — enhancing decision-making for tech purchases through an intuitive Flask interface.

Python Flask Pandas NumPy Scikit-learn
// 01
Demo GitHub
Multivariate Analysis of Liver Disease

Applied advanced multivariate statistical techniques — PCA, Factor Analysis, Discriminant Analysis, and Canonical Correlation Analysis — to deeply analyse the determinants of liver disease.

Python Pandas Scikit-learn NumPy Jupyter Notebook
05

Get In Touch

I'm open to exciting roles in AI/ML engineering, full-stack development, and data science. Whether you have a project in mind or just want to connect — feel free to reach out.

✉
Email
vimanshamj@gmail.com
 📞
Phone
+94 712 304 489
 in
LinkedIn
linkedin.com/in/vimanshaMJ
 📍
Location
Maharagama, Sri Lanka