Girma Bewketu

Addis Abeba, Ethiopia

Contact Information

Professional Summary

Highly skilled and dedicated Machine Learning Engineer and Full Stack Developer with a Master’s degree in Computer Science from Bahir Dar University. Over 5 years of experience delivering intelligent solutions across domains like NLP, computer vision, and predictive analytics using deep learning and machine learning techniques. Demonstrated ability to lead projects from ideation to deployment in both backend systems and AI pipelines. Proven success in AI applications for healthcare, finance, language translation, and mobile services.

Education

MSc in Computer Science
Bahir Dar University, Bahir Dar, Ethiopia
Graduation Year: 2021

Professional Experience

Freelance AI/ML Engineer

Remote | 2024 – Present

  • Pharmacy Management System: Developed a full-featured pharmacy management system to streamline operations including inventory tracking, prescription handling, and sales reporting.
    TechStack: Spring Boot, PostgreSQL, Hibernate, REST APIs, Maven
  • Chatbot for Telebirr: Developed an intelligent multilingual chatbot using NLP and deep learning for Ethiopia’s mobile money platform.
    TechStack: Python, Flask, Deep learning, TensorFlow
  • Public Stance Detection: Created ML models to classify social media content based on public opinion.
    TechStack: Machine Learning, Scikit-learn, Pandas, Python
  • Machine Translation System: Built machine translation engines for Ethiopian languages using deep learning models.
    TechStack: Python, TensorFlow, Keras, Deep Learning
  • Customer Churn Prediction: Designed machine learning pipelines to predict customer retention patterns.
    TechStack: Machine Learning, Scikit-learn, Python
  • Sentiment Analysis: Created sentiment analysis model for analyzing opinions in Amharic and Oromifa texts.
    TechStack: Python, TensorFlow, Keras
  • Diabetes Prediction System: Developed a medical diagnostic model for early diabetes detection.
    TechStack: Python, Scikit-learn
  • Customer Segmentation: Applied unsupervised learning techniques (e.g., K-means clustering) to segment customers based on purchasing behavior and demographics.
    TechStack: Python, Scikit-learn, Pandas, Matplotlib
  • Computer Vision Projects:
    • Object Detection with YOLO: Real-time detection using YOLO architecture.
    • Breast Cancer Detection: Deep learning model for classifying cancerous tissue from imagery.
  • Data Annotation Platform (Mobile): Designed and deployed a mobile-based platform for data annotation to support machine learning datasets. Integrated user-friendly interfaces for efficient labeling and real-time synchronization.
    Technologies used: Flutter, Laravel, MySQL
  • E-Learning Platform (Mobile): Built a mobile e-learning platform with interactive course content, assessments, and progress tracking. Ensured compatibility across devices and optimized performance for low-resource settings.
    Technologies used: Flutter, NestJS, MySQL
Super Soft Solution

2023 – 2024

Designed and developed multiple management systems using CodeIgniter, Laravel, and NestJS including:

  • Health Information Management System
  • Land Information Management System
  • Student Information Management System
  • Health Information Management System
AI Engineer – United States of Africa

2023 – 2024

Project: Zumardi Video Conferencing App

  • Designed and implemented speech-to-text machine translation systems focused on the Amharic language.
  • Integrated models into a real-time conferencing platform.

TechStack: Python, Deep Learning, NLP

Full Stack Developer & Software Project Manager – Abrak Medical Service PLC

2022 – 2023

Project: Electronic Medical Records (EMR) System

  • Project Oversight: Led the end-to-end development of an EMR system, ensuring alignment with healthcare industry standards and client requirements.
  • Team Management: Supervised a cross-functional team of developers, testers, and designers to deliver project milestones on schedule.
  • Stakeholder Communication: Coordinated with healthcare professionals to gather system requirements and provided regular updates to company leadership.
  • Technical Implementation: Oversaw the integration of key features, including patient management, appointment scheduling, medical history tracking, and billing modules.
Backend Developer – Perago Information Systems PLC

2022 – 2023

  • Developed and maintained backend services for the E-Services Platform using Nest.js.
  • Collaborated with cross-functional teams to implement robust and secure APIs.
  • Optimized application performance and scalability.
AI Engineer – iCog Labs

2018 – 2019

  • Conducted research and development for the Speech Emotion Recognition project using deep learning technologies.
  • Worked on model training, optimization, and deployment for emotion recognition tasks.
Technical Skills
  • Programming Languages: JavaScript, PHP, Python, Java
  • Frameworks & Libraries:Spring Boot, ReactJs, NodeJs, NestJs, CodeIgniter, Laravel, Deep Learning (TensorFlow, PyTorch)
  • Databases: MySQL, PostgreSQL, MongoDB
  • Tools & Platforms: Git, RESTful API Development
  • Soft Skills: Problem-solving, Team collaboration, Effective communication

Projects

Perago Information Systems PLC

Project: E-Services Platform

Developed a comprehensive backend system to manage electronic services for various clients.

  • Position: Junior Back-End Engineer
  • Activities: Design, develop, and deploy back-end development
  • Technologies: Angular, .NET Core, MS SQL Server, Azure, Nest.js
Super Soft Solution – Health Information Management System

Built a user-friendly platform to manage health records.

  • Position: Full Stack Developer
  • Activities: Design, develop, and deploy the system
  • Technologies: Laravel, MySQL
Super Soft Solution – Land Information Management System

Developed a system for efficient land management processes.

  • Position: Full Stack Developer
  • Technologies: CodeIgniter, PostgreSQL
Super Soft Solution – Student Information Management System

Implemented features for managing student records and performance.

  • Position: Back-End Engineer
  • Activities: Design, develop, and deploy back-end development
  • Technologies: React.js, Nest.js, PostgreSQL
Super Soft Solution – Exit Exam Management System

Led the backend development using Nest.js to facilitate online exit exams.

  • Position: Back-End Engineer
  • Activities: Design, develop, and deploy back-end development
  • Technologies: React.js, Nest.js, MySQL
iCog Labs – Speech Emotion Recognition

Researched and developed deep learning models to identify emotions in speech data.

Integrated the models into prototype applications for testing and demonstration.

  • Position: AI Engineer
  • Activities: Design and develop Speech Emotion Recognition model
  • Technologies: TensorFlow, Keras
Chatbot for Telebirr

Developed an intelligent multilingual chatbot using NLP and deep learning for Ethiopia’s mobile money platform.

TechStack: Python, Flask, Deep Learning, TensorFlow

Public Stance Detection

Created ML models to classify social media content based on public opinion.

TechStack: Machine Learning, Scikit-learn, Pandas, Python

Machine Translation System

Built machine translation engines for Ethiopian languages using deep learning models.

TechStack: Python, TensorFlow, Keras, Deep Learning

Customer Churn Prediction

Designed machine learning pipelines to predict customer retention patterns.

TechStack: Machine Learning, Scikit-learn, Python

Sentiment Analysis

Created sentiment analysis model for analyzing opinions in Amharic and Oromifa texts.

TechStack: Python, TensorFlow, Keras

Diabetes Prediction System

Developed a medical diagnostic model for early diabetes detection.

TechStack: Python, Scikit-learn

Customer Segmentation

Applied unsupervised learning techniques (e.g., K-means clustering) to segment customers based on purchasing behavior and demographics.

TechStack: Python, Scikit-learn, Pandas, Matplotlib

Computer Vision Projects

Object Detection with YOLO: Real-time detection using YOLO architecture.
Breast Cancer Detection: Deep learning model for classifying cancerous tissue from imagery.

Achievements and Certifications