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AI Engineer with less than a year in Machine Learning, LLM Applications & Computer Vision.
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Computer Science with Artificial Intelligence undergraduate and AI/ML Engineer Intern who builds practical systems and explores applied AI research. Experienced across machine learning, LLM applications, computer vision, RAG pipelines, MLOps, and full-stack AI development. Interested in developing research-informed, production-ready AI systems for business, agriculture, scientific discovery, and real-world decision support.
Coventry University
BSc (Hons) Computer Science with Artificial Intelligence · Computer Science with Artificial Intelligence
August 1, 2026 – Present
National Institute of Business Management - Kandy Innovation Centre
Higher National Diploma in Computer Science with AI · Computer Science with AI
August 1, 2025 – June 30, 2026
National Institute of Business Management - Kandy Innovation Centre
Diploma in Computer Science with Artificial Intelligence · Computer Science with Artificial Intelligence
August 1, 2024 – June 30, 2025
Fintelex Pvt Ltd
AI/ML Engineer Intern
May 1, 2026 – Present
Colombo, Western Province, Sri Lanka
Tomato Leaf Disease Classification & Severity Analysis
June 1, 2026 – June 30, 2026
Built an end-to-end plant disease analysis system combining EfficientNetB0 classification (97.34% test accuracy) with OpenCV image segmentation to quantify diseased vs healthy leaf area as a percentage Validated transfer learning over custom architectures: EfficientNetB0 outperformed MobileNetV2 (94.08%) and a custom CNN (89.35%) on a limited agricultural dataset with class imbalance Integrated Grad-CAM to highlight infection regions, bridging black-box model output with interpretable visual decision support for end users
View ProjectCampaign Response Prediction | End-to-End MLOps Pipeline
June 1, 2026 – June 30, 2026
Designed a recall-optimized ML pipeline for bank-marketing campaign response prediction - 45,211 records, 17 features, 1:7 class imbalance - framing false negatives as the primary business cost Benchmarked Logistic Regression, Random Forest, and XGBoost with class-imbalance handling; XGBoost achieved 86.8% recall and 85.9% accuracy, outperforming baselines on the minority class Built a full MLOps stack: experiment tracking with MLflow, REST API deployment via FastAPI, Dockerised containers, GitHub Actions CI/CD pipeline, and automated pytest test suite
View ProjectMercura AI | Full Stack AI E-Commerce Platform
June 1, 2026 – June 30, 2026
Built a LangGraph ReAct shopping agent with PostgreSQL JSONB session memory and tool-based workflows for product search, cart management, order tracking, and return processing. Engineered hybrid semantic search combining pgvector cosine similarity and PostgreSQL trigram matching with Reciprocal Rank Fusion - handles both natural-language queries and exact product name lookups Built a Demand Intelligence Engine that logs and embeds customer search queries, identifies catalog gaps via cosine similarity thresholds, and generates Gemini-powered inventory recommendations for merchants
View ProjectATIO - Food Systems Decision Intelligence Platform
January 1, 2026 – February 28, 2026
Built a Gemini-powered RAG chatbot for data-driven agricultural decision support Integrated agricultural knowledge retrieval to support data-driven recommendations for food-system challenges. Collaborated in a hackathon environment to design an AI-based solution for practical agriculture and decision intelligence use cases.
Forest Cover Loss & Urban Expansion - Satellite Image Study
January 1, 2015 – December 31, 2025
Designed an 11-year (2015–2025) multi-temporal remote-sensing pipeline quantifying environmental change in the Kandy–Mahaweli Corridor using Landsat 8 and Sentinel-2 with NDVI, NDBI, and MNDWI spectral indices Processed 22 seasonal composites with water masking, median filtering, and percentile normalization to isolate forest cover loss from seasonal vegetation variation Quantified 1.26 km² of localized forest loss and a 44.7% increase in built-up area (0.47→0.68 km²); produced change-detection maps; abstract submitted at iPURSE UOP
View ProjectIntroduction to Responsible AI
Google Cloud Skills Boost
June 1, 2026 – Present
Introduction to Generative AI
Google Cloud Skills Boost
June 1, 2026 – Present
Introduction to Large Language Models
Google Cloud Skills Boost
June 1, 2026 – Present
Qdrant Essentials
Qdrant
February 1, 2025 – Present
Cultural Fit Analysis
The candidate exhibits a strong cultural fit for an innovative and fast-paced AI engineering environment. Their diverse projects span e-commerce, finance, agriculture, and environmental intelligence, demonstrating adaptability and a broad interest in applying AI to various domains. The proactive pursuit of certifications and ongoing research indicates a self-driven individual committed to continuous learning and staying current with industry trends, which is highly valuable in an AI role. The hackathon experience further highlights their ability to collaborate and deliver under pressure.
Soft Skills & Operational Fit
The candidate's project descriptions indicate strong problem-solving skills, particularly in addressing real-world challenges like class imbalance in ML or hybrid search. Their involvement in hackathons and leadership roles suggests good collaboration and communication potential. The detailed project descriptions also reflect an organized and methodical approach to development and research.