MSc student in AI and Data Science with hands-on experience building machine learning models, deploying Al-powered applications, and working across real-world software projects. Comfortable working with Python, TensorFlow, and cloud tools like GCP to design, test, and implement intelligent systems.
Software Developer
Highlights
Designed and implemented a scalable AI-based mobile application, implementing feature-based architecture with Kotlin, Jetpack compose and GCP improving UI efficiency by 20%
Deployed a Python script to integrate the SportsMonk API, enabling dynamic fixture retrieval with filtering and pagination improving data sync speed by 60%.
Frontend Engineer
Highlights
Engineered an Al-powered pet assistant, embedding a ChatGPT wrapper in FlutterFlow, enhancing response latency by 40%.
Upskilled in React and Node.js within 1 month, developing fully responsive and functional frontend UIs that improved user experience and met tight deadlines, delivering projects 50% faster.
Software Developer
Highlights
Created responsive Flutter Web applications, ensuring cross-device compatibility and improving efficiency, reducing load times by 20%.
Constructed high-performance mobile applications using FlutterFlow, leading to the successful deployment of multiple prototypes within a 2-month timeline, which helped onboard 5 more partners.
Software Developer
Highlights
Developed five Al mobile applications across multiple sectors, including multiple apps in the health sector
Facilitated efficient data management with RESTful APIs, enabling seamless integration of user authentication flows, resulting in a 15% increase in user engagement within the initial month.
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MSc
Artificial Intelligence and Data Science
Courses
Understanding Artificial Intelligence
Fundamentals of Data Science
Applied Artificial Intelligence
Big Data and Data Mining with SQL
Research and Application in AI and Data Science for Healthcare
Supervised Learning concepts like Linear & Multiple Regression, SVM, CNNs, NLP.
Python, NumPy, Pandas, Scikit-learn, TensorFlow, PyTorch, Jupyter Notebooks, Flutter, Kotlin.
Complete AI & Machine Learning, Data Science Bootcamp, An Introduction to Statistical Learning, Calculus 1 by Khan Academy, Practical Deep Learning by fast.ai.
Summary
Built a machine learning model to predict housing prices, utilizing advanced techniques such as feature engineering and models like Random Forest and Ridge. Achieved an R² score of 92% by extracting and creating new features to enhance model performance.
Summary
Developed a 5-layer CNN to classify six types of vehicle damage including dent, scratch, and tire flat. Achieved 0.94 AUC and 0.72 precision using data augmentation, dropout, and class weighting for imbalance handling.