Shahab Uddin

Recent Computer Science graduate specializing in Artificial Intelligence with hands-on experience building and deploying Machine Learning solutions through multiple AI engineering internships (Decode Labs, Austify, CodeAlpha). Proficient in Python, data preprocessing, model evaluation, and Streamlit deployment, with a focus on Computer Vision, NLP, and predictive modeling. Driven and analytical, seeking a Junior AI Engineer or ML Engineer role to contribute to real-world, scalable AI systems.

AI system designer at Decodelabs (2026-05 – 2026-06)

AI & Machine Learning Intern

• Built and optimized predictive Machine Learning algorithms using Python.
• Preprocessed and cleaned large-scale datasets for model training pipelines.
• Collaborated on data-driven challenges and presented analytical insights to the development team.

Data scientist at Auspify (2026-07 – 2026-08)

Data Science & Machine Learning Intern

• Content Recommendation Engine: Engineered a content-based recommendation system using TF-IDF vectorization and cosine similarity on text metadata ("feature soup") to suggest top-5 titles.
• Classification & Data Leakage Prevention: Built Logistic Regression and Random Forest models to predict content types (Movie vs. TV Show), implementing custom text cleaning and feature dropping to prevent synthetic data leakage.
• Hyperparameter Tuning: Developed an age rating classification model (Kids, Teens, Adults), improving baseline accuracy from 64% to 69.68% using 3-fold cross-validation with GridSearchCV on Random Forest classifiers.
• Automated Data Pipelines: Designed an automated, end-to-end preprocessing pipeline with Scikit-Learn's ColumnTransformer (median imputation, standard scaling, and One-Hot Encoding) to convert raw metadata into a 123-feature model-ready matrix.

Bachelor of Science in Computer Science (Specialization in Artificial Intelligence) – Abdul Wali Khan University Mardan (2022-09 – 2026-06)