+3
Years Experience
Hello, I'm
Machine Learning
Engineer
Machine learning engineer focused on practical AI systems, data-informed products, and clean, scalable implementation.
+3
Years Experience
+10
Projects on GitHub
4
Clients
Data Analytics
Predicting customer churn and highlighting the highest-risk segments for retention actions.
Web Application
A productivity-focused application for organizing tasks, priorities, and daily execution.
AI / IoT
An AI-assisted home security concept for detecting suspicious activity and surfacing actionable alerts.
Data Engineering
A starter repository for structured analytics work, reproducible notebooks, and clean project setup.
Current
Independent + Sphere Solution Developers
Building end-to-end ML projects and collaborating with other Sphere developers in group-based project creation and implementation.
Aug 2025 - Nov 2025
Multiple Industry Simulations
Completed virtual job simulations across software engineering and data science tracks with practical delivery tasks and documented outputs.
November 12, 2025
August 20, 2025
August 18, 2025
August 15, 2025
March 3, 2026
June 2024 - September 2024
REREC (ICT Department)
Supported ICT operations and day-to-day technical tasks, including troubleshooting, systems support, and practical IT service delivery.
Sep 2021 - Oct 2025
Multimedia University of Kenya
Built strong foundations in computing, problem-solving, and analytical thinking that support machine learning engineering and software development practice.
Core language for ML pipelines, model training, and evaluation workflows.
Essential for interactive web applications and frontend logic.
Reliable toolkit for classical machine learning and rapid experimentation.
Deep learning framework for neural network modeling and deployment.
Data wrangling and feature engineering for structured datasets.
High-performance numerical operations used across ML and analytics tasks.
Notebook environment for reproducible analysis and model iteration.
Data extraction and transformation for analytics and model-ready datasets.
Version control, collaboration, and project portfolio publishing.
Containerized environments for reproducible ML development and deployment.
How feature selection and clean data pipelines improve retention prediction quality.
Read MoreWhat I learned about balancing simplicity, usability, and incremental delivery.
Read MoreBuilding reliable detection workflows while keeping interfaces intuitive.
Read More