Ai Engineer Computer Science

HUY HOANG DAO

+84-982****** ****************@*****.*** GitHub

AI ENGINEER

SKILLS

• Programming Languages: Python, Java, C#, JavaScript

• AI/ML Frameworks & Tools: PyTorch, TensorFlow, Keras, Scikit-learn, YOLOv5, OpenCV, LangChain, FastAPI, Git, RESTful APIs

• AI/ML Techniques: Deep Learning (CNN, RNN, LSTM, Transformer), Grad-CAM, NLP, LLMs, RAG, Prompt Engineering, Model Optimization

• Databases: SQL Server, MongoDB, PostgreSQL

• Others: Agile/Scrum, Teamwork, Clean Code Practice, Real-world AI Workflow Understanding EDUCATION

Industrial University Of Ho Chi Minh City Ho Chi Minh, VN Bachelor of Science in Computer Science August 2021 - Expected Graduation: December 2025 Cumulative GPA: 3.2/4.0

Relevant Coursework: Programming; Database Management; Deep Learning (CNN, RNN, LSTM, Transformer, ...); Machine Learning & Data Analysis; Computer Vision & NLP, Project Management WORK EXPERIENCE

Wata Solution Ho Chi Minh, VN

AI Engineer Intern April 2025 - Present

• Built an internal chatbot for the company using RAG (Retrieval-Augmented Generation) with LangChain, integrating LLMs via OpenRouter API.

• Responsible for the entire RAG pipeline and backend, including document ingestion, vector storage with ChromaDB, and embedding using sentence-transformers.

• Developed backend APIs using FastAPI, applied clean coding practices and maintained modular project structure.

• Designed and implemented PostgreSQL database schema to store vehicle and parking information.

• Built Smart Parking App using YOLOv5 to detect license plates with 96.22% accuracy; developed APIs for processing and managing check-in/out data.

• Set up local development environment with venv for clean deployment and reproducibility.

• Gained practical experience with real-world AI pipelines, improved teamwork skills, and strengthened backend development capabilities.

PROJECTS

Pneumonia Detection (2025)

- Developed a deep learning model for pneumonia detection using chest X-ray images. The system classifies images into pneumonia and non-pneumonia categories using PyTorch and Scikit-learn. Achieved 93% accuracy and implemented Grad-CAM visualization to interpret model predictions. Deployed the model as a web application, allowing users to upload X-ray images and receive prediction results with heatmap overlays. Integrated a RESTful API using FastAPI and developed a responsive user interface using ReactJS. (Individual Project)

- Technologies: PyTorch, Scikit-learn, FastAPI, ReactJS, Grad-CAM, OpenCV

- Role: AI Engineer, Full-Stack Developer (Collected and preprocessed medical imaging data, trained and fine-tuned a deep learning model for pneumonia classification, developed and deployed the backend using FastAPI, built the frontend with ReactJS, and integrated it with the backend API.)

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