RAKIN
Senior Full Stack Engineer

Shakeef Ahmed Rakin

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Senior Full Stack Engineer with over 2 years of professional experience and 4+ years of project depth. I architect end-to-end SaaS products across web, mobile, and desktop, and ship production-grade AI/ML pipelines.

About Me
Shakeef Ahmed Rakin | Hero Image

What I work on

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Full Stack Development

  • Architecting SaaS products end to end
  • Migrating legacy stacks to modern foundations
  • Designing public APIs for teams and AI clients

AI & Machine Learning

  • Shipping production AI pipelines
  • Training and benchmarking custom models
  • Wiring AI into real product flows

Mobile & Desktop

  • Mobile apps sharing one backend with web
  • Desktop tools for industrial and research work
  • Open source templates for cross platform delivery

Where I've worked

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

  1. 1 monthMay 2026 – Present

    Senior Full Stack Developer

    ve2max

    • Engaged as a full stack developer across multiple products within the VE2 ecosystem
    • Collaborating with cross-functional teams in a distributed environment on scalable web application development
  2. 1 year 7 monthsOct 2024 – Apr 2026

    Full Stack Developer

    Podcas

    • Architected web (Next.js) and mobile (React Native) apps from the ground up
    • Led migration from legacy Supabase stack to PostgreSQL + Drizzle ORM
    • Built end-to-end AI podcast generation pipelines with LLM scripting and multi-provider TTS
    • Designed a production multi-region daily news podcast system
    • Shipped a public OpenAPI-spec'd REST API and MCP server for programmatic podcast generation
  3. 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

Hackathons and awards

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Hackathons, datathons, and competitions from my undergrad years.

  • 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

Things I've built

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A showcase of my completed and ongoing work across web, desktop, mobile, and machine learning.

Papers I've co-authored

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Papers I've co-authored with collaborators across multiple 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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Recent writing

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