Gibran Ali - Software Engineer

Hello! 👋

I am a Software Engineer (and Doctor) with 10+ years of experience building large-scale community platforms, real-time systems, and distributed backend services.

Architected and operated systems used by 13M+ users across 48,000 communities. Experienced with Node.js, React, Microservices, WebSockets, and AI-driven analytics.

My CV

Harbor

Harbor is a CRM and marketing automation platform that connects all audience data into a singular source of truth through no-code gamified digital experiences. Powers over 10 Million Users.

Full Stack Software Engineer

2023 - 2026

Shoob

Shoob is a unique and robust discord bot card game in over 48000 discord servers and a reach of 13 million discord users!

Founder and Lead Developer

2017-2024

Animesoul

Animesoul is the Worlds Largest Anime Discord Server. Over 750,000 users!

Founder and Lead Developer

2016-2024

WarrisKids

WarrisKids is a Pediatric Clinic serving hundreds of patients as a custom platform to manage histories, follow-ups, appointments, video consultations, & process payments + much more.

Founder and Lead Developer

2022 - Ongoing

MBTT

Created the software running the Medical Board of Trinidad & Tobago, digitized the national doctors register, integrated payments into the platform reducing in-person visits.

Project Lead & Developer

2021-2025

☝️🤓 Currently Learning

Game engine development
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Started development on a basic custom game engine. Completed so far are sprite interactions, branching NPC dialogue support, a scheduling system that allows for dynamic events/timed events in game, multi threaded world rendering, camera bounding limits etc.
AI Media Generation Pipelines
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Able to optimize ComfyUI pipelines for increased accuracy of prompt generations. Can not only generate basic game assets of the same consistent art style, but can also generate multiple angles/views of the same asset. Lots of work (and potential) here.
ML & RAG Pipelines
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Able to create a simple RAG nodejs AI app that loads context from a database and respond to questions based on the predefined context. This is ideal for embedded FAQ chatbots that lay users can modify responses easily from a web dashboard. Able to also optimize parameters to reduce hallucinations to near zero and currently learning model training to bypass RAG entirely for less dynamic applications and to increase performance.