About
I’m an aspiring AI Engineer with a strong background in software and web development, with a growing focus on building practical machine learning applications. My work spans from developing data pipelines and predictive models to creating interactive dashboards that make insights easy to understand.
At the moment, I’m developing a Crime Prediction Dashboard using real data from Bristol, where I built a pipeline to forecast monthly crime patterns and deployed it through Streamlit. Alongside this, I work as an AI Data Trainer, supporting the training and fine-tuning of large language models(LLMs) through prompt engineering, response evaluation, and quality checks.
Previously, I’ve worked as a Web Developer Intern and Junior Software Engineer, gaining hands-on experience with React, Spring Boot, and Flutter. I’ve also delivered academic and personal projects, including a Household Energy Simulation using reinforcement learning and a Music Recommendation System powered by Graph Neural Networks(GNN). These experiences helped me strengthen both my technical skills and problem-solving mindset.
I enjoy solving real-world problems through data, experimenting with new technologies, and building tools that bring practical value. Outside of work, you’ll usually find me cycling or bowling.
- Python
- R
- SQL
- Pandas
- NumPy
- Matplotlib
- Scikit-learn
- TensorFlow
- Tableau
- Streamlit
- OpenAI Gym
- Docker
- MongoDB
- Firebase
- PostgreSQL
- Git
- Google Cloud
- AWS (learning)
Experience
February 2024 - Present - Supported training and fine-tuning of large language models (LLMs) through prompt engineering, response evaluation, and systematic error identification.
- Improved model performance on reasoning, code generation, and instruction-following tasks while ensuring compliance with project guidelines and data privacy standards.
- Prompt Engineeering
- LLM Evaluation
- HTML
- CSS
- JavaScript
January 2023 - July 2023 - Developed WordPress websites using Divi page builder, including custom themes, layouts, plugins, and Gutenberg modules with PHP and JavaScript.
- Conducted quality assurance (QA) testing and applied basic SEO principles to ensure functionality, responsiveness, and visibility.
- Completed a full client website (50+ pages) under tight deadlines, handling custom development, integration, and project management.
- Strengthened problem-solving, attention to detail, and time management skills while managing multiple development tasks.
- PHP
- JavaScript
- Wordpress
- Divi
- Custom Plugins
- Quality Assuarance
- SEO
September 2021 - February 2022 - Developed front-end web applications using ReactJS and NextJS, back-end APIs with Spring Boot, and mobile applications using Flutter.
- Conducted SEO optimisation and generated reports using Java Jasper, while gaining exposure to ERP processes including sales and purchasing documents.
- Utilised internal frameworks to achieve project targets while demonstrating transparency, accountability, and a strong work ethic.
- Java
- Flutter
- SpringBoot
- ReactJS
- NextJS
- Jasper
- SEO
Projects
Developed an machine learning pipeline using real crime data from Bristol. Performed data cleaning, feature engineering (time, location, crime type), and aggregated data at postcode level. Trained XGBoost models to predict monthly crime count and classify likely crime types. Evaluated using F1-score and MSE. Deployed an interactive dashboard via Streamlit to visualise predictions
- Python
- Streamlit
- ML Pipelines
- Scikit-learn
- Matplotlib
- Pandas
- NumPy
Pothole Severity Detection
GitHubThis project implements a pothole severity detection system using YOLOv5 for object detection. I parsed VOC XML annotations to extract bounding boxes around potholes in images, resized them, and converted to YOLO format for training. The dataset was split into train (80%), validation (10%), and test (10%) sets. Data augmentation techniques like horizontal flips and brightness adjustments were explored using Albumentations to enhance model robustness. The YOLOv5 model was trained with hyperparameters such as 416 image size, batch 16, and 50 epochs, achieving effective pothole detection. Evaluation included confusion matrices visualized with Seaborn and Matplotlib to assess classification accuracy across severity levels (Immediate, Moderate, No Immediate Attention). This project showcases my experience in applying Python, computer vision, dataset preparation, and deep learning for real-world applications.
- Python
- PyTorch
- Scikit-learn
- Matplotlib
- Seaborn
- Jupyter Notebook
- YOLO
Music-Recommendation-System
GitHubDeveloped a hybrid music recommendation system combining a knowledge-based system (KBS) with Graph Neural Networks (GNNs) to deliver accurate song recommendations. Using two datasets (song and artist terms), We preprocessed data by handling null values, normalising numerical features like tempo and duration, and creating a knowledge graph with NetworkX, which was converted to a DGL graph for GCN training. The GCN model, built with PyTorch, used two convolutional layers with ReLU activation and was optimised through hyperparameter tuning (learning rate: 0.01, hidden size: 16), achieving a training accuracy of 0.5022. The system recommends the top 5 songs based on song and artist features, evaluated using intra-similarity scores (e.g., 0.9994 for '90s' input). Ethical considerations ensured compliance with GDPR and copyright laws, maintaining data privacy and fairness. This project showcases my expertise in Python, NetworkX, PyTorch, DGL, data preprocessing, and collaborative problem-solving.
- Python
- Jupyter Notebook
- PyTorch
- Knowledge-based Systems (KBS)
- Graph Neural Networks (GNN)
Student Performance Analysis
GitHubFor my university coursework, I developed a machine learning pipeline to predict student academic performance using the Higher Education Students Performance Evaluation dataset, identifying key factors influencing grades for stakeholders like educational institutions and teachers. Through thorough data exploration, I visualised patterns and outliers, preprocessed the data by imputing missing values and removing irrelevant features, and addressed class imbalance using SMOTE and RandomUnderSampler. I selected impactful features via univariate selection and correlation analysis, then trained and optimised models including Logistic Regression, Random Forest (achieving 44.83% accuracy), KNN, Decision Tree, SVM, and RNN using GridSearchCV, evaluating performance with confusion matrices and ROC curves. Adhering to ethical data handling under the dataset’s Creative Commons license, I ensured anonymity and fairness, enhancing my skills in Python, scikit-learn, TensorFlow, and independent problem-solving while delivering actionable insights for educational decision-making.
- Python
- Pandas
- NumPy
- Matplotlib
- Seaborn
- Scikit-learn
- TensorFlow
- Jupyter Notebook
