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ai_mahmoud

Mahmoud E

@ai_mahmoud

AI Engineer, LLM apps, RAG and web platforms on Azure

Egitto
Arabo, Inglese, Spagnolo
Alcune informazioni sono riportate in lingua inglese.
Chi sono
I build AI apps and web platforms and ship them to the cloud. Recent work: a clinic platform used by 10 clinics with an AI prescription assistant; an Arabic study platform for ~1,000 students on Azure, built in 10 days; a ChatGPT tutor connected to Google Sheets. Python, FastAPI, React, PostgreSQL, Azure OpenAI. Tested code, clear docs, support after delivery.... Continua a leggere

Competenze

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ai_mahmoud
Mahmoud E
offline • 

Consulta i miei servizi

Sviluppo di app Android
I will build a mobile app with machine learning and ai features
A partire da100 USD / progettoMaggiori dettagli

Esperienza lavorativa

Self-Employed_/ Freelancer

Freelance Software Engineer

Self-Employed / Freelancer • Freelance

Jul 2026 - Present • 4 mos

Four client systems in production. MyDoc90: clinic platform used by 10 clinics, with 1,195 automated tests and an AI dictation-to-prescription assistant on Azure OpenAI. Al-Madad: Arabic study platform for about 1,000 students, launched solo on Azure in 10 days, with a RAG assistant that cites sources. A ChatGPT French tutor for doctoral research (20 students), built in 2 days on a 10-tool MCP server. Two Flutter/Kotlin Android apps for a team at Orange Egypt.

Microsoft

Data Science Lead, Microsoft Learn Student Club

Microsoft • Part time

Sep 2025 - Aug 2026 • 11 mos

Led data analytics and business intelligence training for 50+ students, focusing on real-world applications. Designed and delivered an end-to-end BI curriculum covering data cleaning, transformation (Power Query), data modeling, and dashboard creation (Power BI). Introduced SQL fundamentals through business-driven scenarios and guided students in defining KPIs and making data-driven decisions.

ScaleHub

AI Code Reviewer, Outlier AI

ScaleHub • Freelance

Jun 2024 - Oct 2024 • 4 mos

Reviewed and improved AI-generated Python code for correctness, clarity, and logical consistency. Labeled and structured datasets for machine learning and NLP tasks under strict quality standards. Identified weaknesses in model reasoning, safety, and factual accuracy, contributing to improved model performance.