Thavamohan Thananchayan
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 less than a year in Python, Deep Learning & NLP.
Results-driven Computer Engineering graduate with strong hands-on experience building, training, and deploying intelligent systems for real-world applications. Proficient in Python, TensorFlow/Keras, Hugging Face, and end-to-end machine learning pipeline development, with solid expertise in deep learning, NLP, and model optimization. Experienced in integrating ML models into scalable backend systems using Spring Boot, PostgreSQL, Docker, and AWS, ensuring production-ready, secure, and high-performance solutions. Passionate about leveraging AI to solve complex problems and deliver measurable impact. Seeking AI/ML engineering roles where I can contribute to innovative, data-driven products and continuously advance in cutting-edge machine learning technologies.
University of Ruhuna
Bachelor of Science in Computer Engineering · Computer Engineering
August 1, 2020 – June 30, 2025
Invicta Innovations (Pvt) Ltd
Software Engineer - Internship
July 1, 2024 – January 1, 2025
Jaffna, Northern Province, Sri Lanka
AI-Powered Resume Analyzer
December 1, 2025 – June 1, 2026
Built a FastAPI backend to process PDF resumes and generate summaries using LoRA-tuned Gemma-3 models. Implemented scoring and job-role classification with PostgreSQL and SQLAlchemy ORM. Containerized the application with Docker and automated builds using GitHub Actions.
Crop Yield Prediction
December 1, 2025 – June 1, 2026
Achieved accuracies of 0.93 (DT) and 0.95 (SVM) predicting yield from rainfall/temperature features.
AI Document Question Answering Bot
December 1, 2025 – June 1, 2026
Developed a Retrieval-Augmented Generation (RAG) based chatbot to answer questions from uploaded PDF documents using LangChain and ChromaDB. Implemented document loading, text chunking, embeddings, and semantic search with Hugging Face sentence transformers and Google Gemini 2.5 Flash. Built a React frontend and FastAPI backend, and deployed the application using Render and Hugging Face Spaces.
Plant Leaf Disease Detection System
December 1, 2025 – June 1, 2026
Trained a CNN on the PlantVillage dataset (21 classes), achieving 94.6% accuracy and a 0.947 macro F1-score. Developed FastAPI and Flask inference APIs for confidence-scored disease prediction with automated image preprocessing. Built React and React Native applications for web and mobile-based plant disease detection.
Secure Results Management System (SRMS)
December 1, 2025 – June 1, 2026
Developed a blockchain-based academic results system using Ethereum smart contracts to ensure tamper-proof records. Integrated IPFS for secure transcript storage and QR-based verification of document integrity. Implemented role-based access control and two-step email verification for secure result management.
Generative AI Applications with RAG and LangChain
IBM (Coursera)
May 1, 2026 – Present
Intro to Machine Learning
Kaggle
May 1, 2026 – Present
Cultural Fit Analysis
The candidate demonstrates a strong academic background with diverse AI/ML projects, including resume analysis, crop yield prediction, disease detection, and RAG-based chatbots. This breadth of project work, combined with an internship in software engineering, suggests adaptability and a willingness to learn new technologies. The focus on AI/ML aligns well with an AI Engineer role, indicating a good cultural fit for an innovation-driven environment. However, the experience is primarily academic, and the candidate's experience level is 0, which might require mentorship in a professional setting.
Soft Skills & Operational Fit
The candidate's project descriptions indicate a results-driven approach and a passion for solving complex problems with AI. The internship experience suggests an ability to contribute to full-stack development, resolve issues, and work within an SDLC, indicating good operational fit. However, direct evidence of communication, teamwork, or leadership in a professional setting is limited to the internship description.