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raymondoyondi/README.md

Hi there 👋 I'm Raymond Oyondi

💡 About Me

I am a Computer Science student at Texas Tech University (Class of 2030) and a freelance software engineer at Raymond Oyondi Consulting. I specialize in bridging the gap between complex data science and scalable cloud architecture. My work focuses on building high-performance systems where every millisecond of latency and every percentage of model accuracy matters.


👨‍💻 What I Do

  • 🚀 Engineering Scalable Backends: I specialize in high-concurrency systems using Go and Node.js, architecting microservices containerized via Docker and orchestrated with Kubernetes. My recent deployments have reduced overhead by 40% through automated CI/CD pipelines.
  • 📊 Predictive Analytics: I build end-to-end Machine Learning pipelines. Whether forecasting NBA stats with 91% accuracy or analyzing market sentiment via TensorFlow, I focus on turning raw data into actionable business intelligence.
  • 📈 Real-Time Data Architecture: I specialize in reactive, low-latency systems. Whether engineering real-time chat applications with sub-100ms latency for concurrent users or managing high-volume data persistence (Postgres, Redis), I focus on 99.9% uptime and seamless data flow efficiency.
  • ☁️ Cloud Infrastructure: I leverage AWS (DynamoDB, Lambda) to ensure 99.9% application uptime, implementing robust OAuth2 security protocols to maintain 100% data privacy compliance.

🎯 My Approach

"I don't just write code that works; I write code that scales."

  • Reliability as a Standard: My background managing household operations and running a self-employed business has instilled a "zero-failure" mindset. I approach software engineering with the same accountability I bring to real-world responsibilities.
  • Performance-Driven Design: I prioritize memoization, server-side rendering, and caching (Redis) to ensure the user experience is as fast as the backend is powerful.
  • Continuous Evolution: As both a student and a professional, I thrive on the "learn-build-deploy" cycle. I am currently exploring the intersection of Time-Series Forecasting and Distributed Systems.

🌐 Socials:

email LinkedIn

💻 Tech Stack:

Languages: Go Java Python C C++ Ruby Dart Rust JavaScript TypeScript

Developer Tools: AWS Docker Kubernetes Firebase Git

Frameworks: Next JS NodeJS Flutter Rails React

Databases: Redis Postgres

Libraries: Pandas NumPy Matplotlib scikit-learn TensorFlow PyTorch

🏆 GitHub Trophies

📊 GitHub Stats:



✍️ Random Dev Quote

🔝 Top Contributed Repo

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  1. Chat-Application Chat-Application Public

    A fully functional, real-time chat application built with Flutter for a beautiful, cross-platform user interface and Firebase for a robust, scalable backend. This project showcases modern mobile de…

    Dart 1

  2. Real-Estate-App Real-Estate-App Public

    A comprehensive digital marketplace built with Ruby on Rails that connects property buyers, sellers, and renters. This application allows users to list properties, browse available real estate, and…

    Ruby 1

  3. Stock-Predictor Stock-Predictor Public

    A sophisticated platform for analyzing and forecasting stock market movements leveraging machine learning (ML) models, technical analysis, and sentiment analysis, all implemented in TypeScript. Thi…

    TypeScript 1

  4. nes-emu nes-emu Public

    A reasonably accurate, cycle-level NES emulator written in Rust. Strives for portability and performance with a focus on matching the behavior described in public documentation like the NESdev Wiki…

    Rust 1

  5. NBA-Player-Performance-Predictor NBA-Player-Performance-Predictor Public

    This project utilizes machine learning to predict player statistics for upcoming seasons. The repository demonstrates the entire data science pipeline and is built to power an interactive web dashb…

    Python 1

  6. Movie-Reservation-System Movie-Reservation-System Public

    A movie reservation system built with Go that leverages a RESTful API architecture to manage the complexities of seat reservations, payment processing, and real-time availability.

    Go 1