YASIDA INSIKA WANIGATUNGA
AI EngineerAI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
AI Engineer with 2+ years in Machine Learning & Cloud Computing
I am a dedicated, responsible, and hardworking individual with a strong interest in software development, data engineering, machine learning, and cloud computing. I am passionate about learning new technologies and applying my skills to solve real-world problems. I have hands-on experience in web automation, machine learning, deep learning, large language models, computer vision, and backend development. I am a fast learner, strong team player, and comfortable working on challenging technical tasks.
Institute of Computer Engineering
Comprehensive Master Java Developer
August 1, 2022 – June 30, 2023
University of Moratuwa
Bachelor of Business Science (B.B.Sc.) · Decision Science
August 1, 2021 – June 30, 2024
CIMA
Dip in MA
August 1, 2020 – Present
Sri Sumangala College
G.C.E. Ordinary Level and G.C.E. Advanced Level
June 1, 2006 – May 31, 2019
Jahan.ai
Software Engineer
February 1, 2025 – Present
India
Innodata Lanka
Trainee Software Engineer
September 1, 2023 – January 1, 2025
Colombo, Western Province, Sri Lanka
Final Year Project: RCC-Enhanced Graph Neural Networks for Molecular Classification
June 1, 2026 – June 1, 2026
Co-authored a research paper titled "Uncovering Structural Hierarchies in Molecules with Rich Club-Informed Representation Learning." Developed an RCC-based structural plugin for Graph Neural Networks to improve molecular classification performance. Improved accuracy on benchmark datasets while mitigating oversmoothing and maintaining model stability. Tech Stack: Python, Graph Neural Networks, PyTorch, Machine Learning, Molecular Classification.
Airline Delay and Cancellation Data Analysis
June 1, 2026 – June 1, 2026
Built predictive models using Random Forest Regression with PySpark to analyze airline delays and cancellations. Integrated data preprocessing and visualization through Azure services and Power BI for actionable insights. Tech Stack: Random Forest Regression, PySpark, Azure Blob Storage, Azure Analysis Services, Power BI, RMSE.
Student Exam Performance Prediction
June 1, 2026 – June 1, 2026
Developed an end-to-end machine learning pipeline for student performance prediction using CatBoost, Random Forest, and Logistic Regression. Deployed the model using Flask for real-time predictions through a user-friendly web interface. Tech Stack: Python, Pandas, Scikit-learn, Flask, HTML, AWS.
Retail AI Solutions Dashboard for Retail Organization
June 1, 2026 – June 1, 2026
Built AI-powered retail solutions using K-means clustering to personalize product suggestions, shopping lists, and promotions. Integrated chatbot and virtual assistant features to improve customer experience. Tech Stack: K-means, PCA, RAG, LangChain, OpenAI, Streamlit.
AI Powered Research Assistant
June 1, 2026 – June 1, 2026
Developed a research assistant to summarize literature, analyze trends, answer queries, narrate papers, and compare research papers. Built a user-friendly interface using React with backend AI integration. Tech Stack: PyTTSx3, NLP, OpenAI, RAG, LangChain, Flask, React.
Supermarket Product Sales Data Prediction Model
June 1, 2026 – June 1, 2026
Developed a time series forecasting model using Darts, NBEATS, and regression models. Used Pandas, NumPy, and Matplotlib for data cleaning, analysis, and visualization. Tech Stack: Python, Darts, NBEATS, Pandas, NumPy, Matplotlib, Seaborn, R2 Score.
Uncovering Structural Hierarchies in Molecules with Rich Club-Informed Representation Learning.
Unknown
June 1, 2026 – Present
Cultural Fit Analysis
The candidate's academic projects demonstrate a strong interest in diverse applications of AI, from molecular classification to retail solutions and research assistants. Participation in hackathons (4th Place in Datastorm 5.0, Finalist of IntelliHack NextGen 2024) indicates a proactive and competitive spirit, and a willingness to engage with challenging problems. The leadership roles in university societies suggest a collaborative and community-oriented mindset. The breadth of skills and project diversity indicate adaptability and a strong learning curve, which are positive indicators for cultural fit in an innovative AI team.
Soft Skills & Operational Fit
The candidate's self-description highlights being dedicated, responsible, hardworking, a fast learner, and a strong team player. Project descriptions indicate collaboration and problem-solving. The leadership experience as an Orchestra Lead Guitarist and Member of the Society of Business Analytics suggests organizational skills, teamwork, and communication abilities. These traits align well with operational fit in a dynamic technical environment.