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MARTIN NJOROGE
🇰🇪Kenya (UTC+03:00)
Created AtUpstaffer since November, 2025

MARTIN NJOROGE — Software Engineer and AI Training Specialist

Expertise in AI and Machine Learning (2.0 yr.), Full Stack Web (1.0 yr.).

Last verified on November, 2025

Core Skills

Bio Summary

Proven Software Engineer with robust academia in Intelligent Systems & Software Engineering, adept in Python, Java, and AI/ML technologies. Offers hands-on experience in AI model training and software development, with expertise in algorithm optimization and system design. Noteworthy involvement in developing an AI-augmented code optimization framework and a performance predictor project during BSc. Combines technical prowess with data annotation and model evaluation finesse, poised to thrive in AI innovation.

Technical Skills

Programming LanguagesJava, JavaScript, PHP, Python
C++ Libraries and ToolsC/C++/C#
UI Frameworks, Libraries, and BrowsersHTML/CSS Preprocessors
AI & Machine LearningNeural Networks, NLP, PyTorch, Scikit-learn, TensorFlow
Python Libraries and ToolsPyTorch, Scikit-learn, TensorFlow
.NET PlatformWPF
Databases & Management Systems / ORMMySQL
Methodologies, Paradigms and PatternsArchitecture and Design Patterns
Virtualization, Containers and OrchestrationDocker
Version ControlGit
PlatformsLCDP
Operating SystemsLinux, Unix
Third Party Tools / IDEs / SDK / ServicesMicrosoft Visual Studio Code
Other Technical SkillsData Structures, Tools

Work Experience

Bachelor of Science Student, AI-Based Academic Performance Predictor

Duration: Final-Year Project, 2020–2023
Summary: AI-Based Academic Performance Predictor using machine learning techniques to forecast student performance.
Responsibilities: Utilized supervised learning algorithms for model training and evaluation, reinforcing early interest in applied AI.
Technologies: Python, Machine Learning Techniques

Software Development Intern, Software Development Internship

Duration: January 2022 – December 2022
Summary: Development, testing, and maintenance of systems for internal university operations as part of ICT department.
Responsibilities: Assisted in building responsive web solutions, debugging, documenting, and optimizing existing systems for performance improvement, contributed to automation tools for administrative efficiency.
Technologies: PHP, JavaScript, HTML/CSS, MySQL

Master of Science Student, AI-Augmented Code Optimization and Predictive Debugging Framework

Duration: Master’s Thesis, 2023–2025
Summary: AI-Augmented Code Optimization and Predictive Debugging Framework exploring how large language models can assist software engineers.
Responsibilities: Developed competency in dataset refinement, annotation, error analysis, and algorithmic research for predictive debugging.
Technologies: Python, Machine Learning, Large Language Models

AI Trainer & Freelance Software Engineering Consultant, AI Trainer & Freelance Software Engineering Consultant

Duration: 2023 to Present
Summary: Freelancing in AI model training, refinement, and evaluation involving structured dataset creation and annotation.
Responsibilities: Train and refine AI models, develop Python-based tools for automation, dataset cleaning, evaluate AI outputs for accuracy, collaborate with R&D teams for AI prototype testing.
Technologies: Python, TensorFlow, Scikit-Learn, PyTorch, NLP techniques

Education

  • Master of Science in Intelligent Systems & Software Engineering
    Exposure to advanced concepts in Machine Learning, Intelligent Algorithms, Deep Learning, Software Architecture, and Human–AI Interaction. Contributed to AI & Innovation Research Group for Python-based tool creation, dataset curation, and evaluation of experimental AI models. Master’s thesis on 'AI-Augmented Code Optimization and Predictive Debugging Framework for Software Engineers'.
    August 2023 – July 2025
  • Bachelor of Science in Software Engineering
    Established a strong technical foundation in programming, system design, databases, and computational thinking. Gained proficiency in various programming languages and developed 'AI-Based Academic Performance Predictor Using Machine Learning Techniques' as a Final-Year Project.
    September 2020 – July 2023

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