AI Engineer · RAG Systems · Multi-Agent Architectures
I design and ship end-to-end intelligent systems, from Arabic-aware NLP pipelines to multi-agent orchestration, using FastAPI, LangGraph, and Google Gemini. Currently building production AI platforms for Jordanian government institutions at 9XAI / HTU.
I'm an AI Engineer based in Zarqa, Jordan, specializing in Retrieval-Augmented Generation (RAG) systems, multi-agent AI architectures, and LLM-powered application development. My work spans prompt engineering, AI agent orchestration, and building end-to-end intelligent systems with FastAPI, LangGraph, and Google Gemini. I apply vibe-coding and AI-assisted development to rapidly deliver full-stack platforms for government and enterprise clients, turning complex AI capabilities into usable, production-grade products.
Python, SQL, Bash, JavaScript
PyTorch, Transformers, Scikit-learn, AI Agents, RAG Systems, LangGraph, LangChain, ChromaDB
Google Gemini (Vertex AI), Qwen, LLaVA, Ollama, Prompt Design, Vibe-Coding
ETL Pipelines, Apache Kafka, Web Scraping, SQLAlchemy, MySQL
FastAPI, Flask
HTML, CSS, Streamlit (AI-assisted full-stack via vibe-coding)
PostgreSQL, MySQL, MongoDB, SQLite, Neo4j / NebulaGraph, Redis
Docker, Git/GitHub, AWS, Linux, Shell Scripting, Vercel
A few of these were built for government institutions under NDA, so descriptions stay at a high level and source code stays private. See public work on GitHub
A regulatory intelligence platform that helps legal and policy teams keep up with fast-changing Arabic and English legislation. It automatically pulls out obligations from new laws, flags where regulations conflict with one another, and tracks how legislation changes over time, cutting down work that would otherwise mean manually cross-referencing hundreds of documents.
A unified oversight platform that gives government decision-makers one place to see how public services are performing across 14+ Jordanian ministries, covering education, health, finance, energy, justice, and the digital economy. It reads through ministry documents with RAG-based search so leaders can ask direct questions instead of digging through reports.
A local-first reading companion for research papers, technical books, and long PDFs. It restructures a dense document into a clean, readable article, and lets you highlight any passage, figure, or equation and ask about it, with the answer pinned in the margin right next to what you asked. Everything runs on-device by default, so documents and conversations never have to leave your machine.
Visit 9XAIPalA satellite-data feasibility study for Jordan's Gulf of Aqaba marine reserve, built to inform ASEZA's coastal management and monitoring decisions. Evaluated 14 satellite and ocean data sources across a 24-year chlorophyll record and a 42-year thermal archive to establish what can, and cannot, be reliably measured from space, then shipped a validated 7-day chlorophyll forecast that consistently beats the seasonal baseline. Built end-to-end by a 5-person team in a 9-day sprint.
View on GitHubA conversational assistant that lets people ask natural-language questions, in Arabic or English, about Jordan's national vision documents and get answers grounded in the actual source text. Low-confidence answers are routed to a human-in-the-loop review step instead of being guessed, so accuracy holds up even on ambiguous questions.
B.S. in Data Science and Artificial Intelligence
Completed the degree in 3.4 years, ahead of the standard track
Oct 2022 - Jan 2026 · Zarqa, Jordan"How I Turned My Weaknesses into Strength"
HTU, Al-Hussein Technical University · 2026I'm open to opportunities in AI engineering, RAG systems, and multi-agent architectures. Reach out directly or connect below.