Profile

Machine Learning Engineer with hands-on experience building production-oriented ML systems, including fraud detection pipelines and real-time computer vision applications. Skilled in model development, data preprocessing, and deployment using Python, scikit-learn, and FastAPI.

Strong focus on building reliable systems, including drift detection and monitoring to ensure model performance in real-world environments.

Projects
Sentinel ML | Fraud Detection & Drift Monitoring Pipeline⁠, End-to-end ML system for detecting fraudulent transactions and monitoring model drift in production.
  • •Built an end-to-end unsupervised fraud detection system using Isolation Forest to handle highly imbalanced credit card data (0.17% fraud rate) without relying on immediate labels.
  • •Engineered a custom Drift Monitoring engine using the Population Stability Index (PSI) to detect distributional shifts in model anomaly scores, preventing "silent failures" in production.
  • •Developed a FastAPI backend with /​predict, /​drift, and /​retrain endpoints, enabling automated model hot-swapping and training history tracking via a file-based registry.
  • •Designed an interactive React dashboard using Recharts to visualize model-aware drift trends and score distributions across weekly transaction batches.
    • Sleep Disorder Risk Predictor⁠, Python, Scikit-Learn, FastAPI, JavaScript
  • •Developed an end-to-end classification system to predict sleep disorder risk (Healthy to Severe) based on lifestyle and physiological data for 100k+ individuals.
  • •Engineered a Scikit-Learn pipeline utilizing RandomForestClassifier with class_weight="balanced" to address significant class imbalance, achieving 89.6% overall accuracy.
  • •Implemented automated preprocessing using ColumnTransformer for mixed data types (StandardScaler for 18 numerical features and OneHotEncoder for 7 categorical features).
  • •Architected a RESTful API using FastAPI and Pydantic for data validation, served via an interactive web interface built with Vanilla JS and CSS.
  • •Performed feature importance analysis, identifying "Sleep Quality" and "Mental Health Status" as the primary drivers of disorder risk, providing transparency into model decision-making.
  • •Built a real-time hand gesture recognition system using MediaPipe and RandomForest classifier
  • •Extracted and processed hand landmark features from live video input
  • •Trained classification model on ASL-based dataset (A–Z + control gestures)
  • •Evaluated performance using confusion matrix and classification metrics
  • •Integrated model into a functional GUI application using Tkinter
  • •Addressed real-time challenges such as lighting variation and detection stability
    • Technical Skills
    Python
    Data Preprocessing and feature engineering
    Fast API
    Scikit-learn
    Model evaluation
    Jupyter Notebook
    Git & Github
    Supervised Learning
    Pandas, Numpy
    Mediapipe
    Software Engineering Experience
    Front-end Developer, D-pia Innovations
  • •Built and maintained responsive client-facing web applications using React, TypeScript, and Tailwind CSS, ensuring cross-device compatibility and consistent UI/​UX
  • •Delivered features iteratively within an agile team, managing code versioning and pull requests via Git/​GitHub
    • 12/2024 – 03/2025Remote, Lagos, NG
  • •Maintained code quality through best practices and peer collaboration under a professional NDA environment
    • Web Developer Intern, Zeroes and One Hi-Tech
  • •Built interactive webpages using core front-end technologies under close mentorship, meeting project deadlines consistently
  • •Contributed to team Git workflows including branching, merging, and code reviews, strengthening collaborative development habits
    • 06/2024 – 11/2024Ilorin, Kwara, NG
  • •Communicated daily with colleagues and supervisors to align on tasks and resolve blockers efficiently
    • Front-end Developer Intern, Sunmence
  • •Developed and deployed a fully responsive waitlist webpage for Buggyhub using React, Tailwind CSS, and Firebase, supporting the product's pre-launch user acquisition
  • •Designed and delivered introductory CSS and JavaScript lessons at Buggybillions coding school, helping beginners write their first functional webpages
    • 12/2022 – 10/2023Ogbomoso, Oyo, NG
  • •Collaborated with a designer and fellow developer to build the main Buggyhub website, maintaining UI consistency and optimizing front-end performance
    • Education
    Artificial Intelligence, SQI College of ICT
    08/2025 – PresentOgbomoso, NG
    B. Tech. in Computer Science, Ladoke Akintola University of Technology
    04/2021 – 09/2025Ogbomoso, Oyo, NG
    Accomplishments
    GDG Hackathon Runner-up, GDG Ogbomoso
  • •Led the Frontend and Authentication development for "freshAl," an Al-powered application designed to detect rot in images of fruits and vegetables.
  • •Collaborated with a team of two developers and designers to build a functional prototype within a 16-hour deadline.
    • 01/11/2023
  • •Showcased my ability to perform under pressure and deliver a high-quality product in a rapid development cycle.