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AI Engineer with 1+ years in Machine Learning & Optimization
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Statistics and Operations Research undergraduate (CGPA: 3.945/4.00) with a strong foundation in Artificial Intelligence, Machine Learning, Optimization, and Software Engineering. Experienced in designing and developing intelligent systems involving Large Language Models (LLMs), Agentic AI, deep learning, reinforcement learning, and predictive analytics. Combines expertise in statistical modeling, optimization, and data-driven decision-making with practical experience in building end-to-end AI applications, intelligent workflows, and decision-support systems. Demonstrated ability to bridge research and implementation through applied work in NLP, computer vision, simulation, explainable AI, and multi-agent systems. Passionate about AI Engineering and building scalable, production-ready intelligent systems that solve complex real-world problems.
University Of Peradeniya
B.Sc (Hons) · Statistics and Operations Research
N/A – June 30, 2026
IT Center - University of Peradeniya
Software Trainee
September 1, 2024 – September 1, 2025
Kandy, Central Province, Sri Lanka
Sabaragamuwa University of Sri Lanka
Student Trainee
October 1, 2023 – January 1, 2024
Sabaragamuwa, Sabaragamuwa Province, Sri Lanka
HR Multi-Agent Task Routing and Memory Engine
June 1, 2026 – Present
Developed a multi-agent AI system using LangGraph, FastAPI, React, and SQLite for automated HR request processing. Implemented intent classification, agent orchestration, memory management (STM/LTM), and context-aware routing. Built specialized agents with fallback reasoning, retry logic, and auditability for robust and traceable AI workflows.
View ProjectAgentic Guest Experience Optimizer
June 1, 2026 – Present
Developed an AI-driven system to predict hotel guest satisfaction and generate personalized pre-stay interventions. Implemented customer segmentation and satisfaction prediction using K-Means, LightGBM, XGBoost, and CatBoost models. Integrated SHAP Explainable AI and an agentic decision pipeline for automated personalized guest communication.
View ProjectPersonal Health Mention Detection in Tweets Using LSTM & Bi-LSTM
June 1, 2026 – Present
Developed deep learning models using LSTM, Bi-LSTM, and Attention mechanisms to classify personal health mentions in tweets. Addressed severe dataset imbalance using Weighted Binary Cross-Entropy, improving minority-class recall and F1-score. Achieved 86% accuracy using a two-layer LSTM with Attention for contextual tweet classification.
View ProjectTea Leaf Disease Classification Using Deep Learning
June 1, 2026 – Present
Developed and compared deep learning models including ResNet50, VGG16, ViT, and hybrid architectures for tea leaf disease classification. Applied transfer learning, fine-tuning, and data augmentation techniques to improve model generalization and performance. Achieved 91.1% classification accuracy using optimized ResNet-based models with Grad-CAM explainability.
View ProjectBiomedical Named Entity Recognition Using Deep Learning
June 1, 2026 – Present
Evaluated Whitespace, NLTK, and WordPiece tokenization, Word2Vec, GloVe, FastText, ELMo, and BERT embeddings, and Softmax and CRF classifiers for biomedical disease entity recognition. Developed a BiLSTM-based NER framework and benchmarked 30 experimental configurations on the BC5CDR dataset. Achieved best performance with BERT + CRF, demonstrating the effectiveness of contextual embeddings for biomedical NER.
View ProjectExploring Health Patterns Using Multivariate Statistical Techniques
June 1, 2026 – Present
Applied multivariate statistical techniques including PCA, Factor Analysis, CCA, SEM, LDA, and QDA on large-scale healthcare datasets. Built a complete analytical pipeline with preprocessing, feature engineering, SMOTE balancing, and statistical modeling. Identified key cardiovascular risk patterns linking obesity, blood pressure, lipid profile, and metabolic indicators.
View ProjectAI/ML Engineer – Stage I & II
Sri Lanka Institute of Information Technology
June 1, 2026 – Present
Actuarial Science - Reserving
S G Actuarial Consultancy Pvt. Ltd.
June 1, 2026 – Present
Makerspace Product Design Course for Entrepreneurs
American Corner Kandy
June 1, 2026 – Present
Cultural Fit Analysis
The candidate's diverse academic projects, involvement in multiple research initiatives, and volunteer experience demonstrate a broad range of interests and a commitment to continuous learning and community engagement. This indicates a strong cultural fit for an innovative and collaborative environment. The pursuit of an 'AI/ML Engineer' certification further highlights dedication to the field.
Soft Skills & Operational Fit
The candidate's project descriptions indicate an ability to work on complex problems, manage data, and apply advanced analytical techniques. Involvement in academic research and volunteer activities suggests a proactive attitude, leadership potential, and a collaborative spirit. The focus on building 'scalable, production-ready intelligent systems' aligns with operational goals for an AI Engineer.