RAKIN
Full Stack Engineer

Shakeef Ahmed Rakin

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Full Stack Engineer from Bangladesh. I build scalable applications across web, mobile, and desktop with hands-on experience in AI and ML. Focused on end-to-end development from system design to deployment, balancing performance and user experience.

About Me
Shakeef Ahmed Rakin | Hero Image

I've been up to a lot of things

More About Me

I write code that solves real problems, one step at a time. Solid in algorithms, data structures, and fluent in TypeScript, Python and C#. Focused on building efficient, clean and reliable systems.

Full Stack Development

Expertise in building end-to-end web applications

  • Frontend & Backend development
  • SEO & Hosting
  • Scalable and efficient architectures
  • Secure API development

AI & Machine Learning

Experienced in working with different AI technologies

  • Natural Language Processing (NLP)
  • Computer Vision applications
  • LLMs (OpenAI, Gemini, etc.)

Mobile & Desktop Apps

Cross-platform development for Android, iOS, and Windows

  • Native and hybrid development
  • Clean, consistent UI/UX

What you can expect

Coding for performance and usability with best practices

  • Simple, intuitive, and user-focused designs
  • Efficient code that aligns with best practices
  • Well documented code for future workability

Where I've worked

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Professional experience building production software across web, mobile, desktop, and AI.

  • 1 year 5 monthsOct 2024 – Present

    Full Stack Developer

    Podcas

    • Architected web (Next.js) and mobile (React Native) apps from the ground up
    • Built end-to-end AI podcast generation pipelines with LLM scripting and multi-provider TTS
    • Led migration from legacy Supabase stack to PostgreSQL + Drizzle ORM
    • Designed a production multi-region daily news podcast system
  • 1 year 3 monthsAug 2024 – Oct 2025

    .NET Software Developer

    Prudence College Dublin

    • Built core modules for HOLOS-IE/EU carbon accounting platforms
    • Implemented IPCC Tier 2 emission factor algorithms for soil and nitrogen modeling
    • Developed Agroforestry and Dairy Cattle Economics modules
    • Contributed to HOLOS-EU modernization with Electron + React + FastAPI

Some of my achievements

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During my undergraduate years, I actively participated in multiple competitive events, including hackathons and datathons, where I collaborated with teams to develop practical solutions to real-world problems.

  • New Academia Learning Innovation 2024

    New Academia Learning Innovation 2024

  • GDSC CRCE BitNBuild’24 Hackathon

    GDSC CRCE BitNBuild’24 Hackathon

  • Kitahack 2024

    Kitahack 2024

  • DevHack 2023

    DevHack 2023

  • MyRapid Bus x UTM Data Hackathon 2023

    MyRapid Bus x UTM Data Hackathon 2023

  • InnoJam 2023 - Smart Sustainable City

    InnoJam 2023 - Smart Sustainable City

I've been building a lot of things

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A showcase of my completed and ongoing projects across web, desktop, mobile, and machine learning. Built with modern tools to solve real-world problems.

Some of my ongoing and published research

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Discover my latest research projects that I've collaborated with esteemed colleagues and institutions

    • PUBLISHED
    • Springer, Cham

    Optimizing American Sign Language Recognition with Binarized Neural Networks - A Comparative Study with Traditional Models

    This undergraduate thesis compares the performance of Binarized Neural Networks (BNNs) against traditional models in the context of American Sign Language (ASL) recognition. The results suggest that BNNs are competitive with traditional models while requiring less computational resources.

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    • PUBLISHED
    • Copernicus Publications

    HOLOSIE - A System Model for Assessing Carbon Emissions and Balance in Agricultural Systems

    HOLOSIE is a system model for assessing carbon emissions and balance in agricultural systems that simulates the carbon cycle and fluxes in agroecosystems. The model is designed to evaluate the impacts of different management practices, such as crop rotation, fertilization, and irrigation, on the carbon balance of agroecosystems.

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    • PUBLISHED
    • SEMARAK ILMU SDN BHD

    Malaysian Sign Language Real-Time Tutorial using CNN Algorithm

    This research aims to develop a real-time Malaysian Sign Language (MSL) tutorial system using Convolutional Neural Networks (CNN) algorithm. The system is designed to provide immediate feedback to users based on their sign language skills.

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    • ONGOING PUBLICATION

    An Optimized Deep-Learning Based Pipeline for Recognition of Sign Language from Low-Resolution Thermal Imagery

    This paper presents an optimized deep learning-based pipeline for recognizing sign language from low-resolution thermal imagery, demonstrating improved performance using Binarized Neural Networks (BNNs) and DenseNet121.

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Read some of my latest blogs

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Explore my latest blog posts on web development, AI, and research projects