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AI Engineer with less than a year in Machine Learning & AI development
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Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Final-year Computer Science undergraduate with hands-on experience in machine learning, deep learning, and full-stack AI application development. Built end-to-end systems including computer vision models, NLP pipelines, and LLM-based RAG applications using Python, TensorFlow, PyTorch, FastAPI, and Flutter. Passionate about building practical AI systems and deploying them in real-world applications.
University of Jaffna
BSc (Hons) · Computer Science
August 1, 2022 – June 30, 2026
CSE Investment Assistant (In Progress)
January 1, 2026 – Present
Built an ML pipeline for Colombo Stock Exchange data processing and prediction. Trained Random Forest and XGBoost models for identifying profitable stock trends.
View ProjectBankIntel AI Chatbot (Banking Intelligent Assistant)
January 1, 2024 – June 1, 2026
Built a hybrid AI chatbot combining a fine-tuned BERT model for intent classification with a RAG pipeline using a locally hosted LLM (Ollama). Enabled document-based Q&A using ChromaDB for semantic search. Implemented an end-to-end pipeline with embeddings, retrieval, and LLM response generation, integrated with a FastAPI backend and Flutter mobile app.
View ProjectAgroVision - Potato Disease Detection
January 1, 2024 – June 1, 2026
Developed a CNN-based potato disease classifier for Early Blight and Late Blight using TensorFlow/Keras, achieving 96.75% test accuracy on the evaluation dataset.
View ProjectBook Recommendation System
January 1, 2024 – June 1, 2026
Developed a content-based recommender using SentenceTransformers and a collaborative filtering system using KNN and SVD to improve recommendation quality.
View ProjectCultural Fit Analysis
The candidate's project diversity, covering areas like finance, healthcare (disease detection), and recommendation systems, indicates a broad interest in applying AI across different domains. The focus on building 'production-ready AI systems' and 'real-world ML applications' aligns well with a results-oriented and innovative culture. The academic background and project-based learning approach suggest a proactive and self-motivated individual. However, without more information on team dynamics or collaborative projects, a full cultural fit assessment is limited.
Soft Skills & Operational Fit
The candidate's resume indicates a strong drive for practical application of AI technologies and a focus on building end-to-end systems. The academic projects suggest an ability to work independently and apply theoretical knowledge to solve real-world problems. However, without specific work experience or psychometric test results, it is difficult to assess collaboration, stress handling, or other operational soft skills.