About Me
AI Engineer with 2+ years of experience building and evaluating production LLM and Retrieval-Augmented Generation (RAG) systems. Skilled in Python, LangChain/LangGraph, vector databases, hybrid search, and agentic tool-u…
AI Engineer with 2+ years of experience building and evaluating production LLM and Retrieval-Augmented Generation (RAG) systems. Skilled in Python, LangChain/LangGraph, vector databases, hybrid search, and agentic tool-use architectures, backed by a BSc in Data Science and hands-on prompt engineering and Arabic/Levantine-dialect model evaluation. Builds end-to-end pipelines spanning document ingestion, embeddings, retrieval, re-ranking, and low-latency inference, delivered through FastAPI, Docker, and CI/CD.
Experience
AI Engineer 1
<p>Designed and deployed a Hybrid RAG Agent routing natural language queries across 10-K PDF reports and a SQL stock price database using LangChain, FAISS, and PostgreSQL.</p><p> Implemented hybrid search, table-aware document ingestion, and a streaming API for efficient, low-latency retrieval over dense financial documents.</p><p> Integrated quantized open-source LLMs to reduce inference latency and serving cost while preserving response quality.</p><p> Reviewed AI-generated solutions for GitHub-based engineering tasks, including bug fixes, feature changes, failing tests, and repository-specific implementation requirements.</p>
AI Prompt Engineer
<p>Created and evaluated prompts for AI models across coding, reasoning, response comparison, Arabic language, and instruction-following task types.</p><p> Performed Arabic and Levantine dialect evaluation, reviewing model responses for dialect accuracy, tone, fluency, and cultural and contextual correctness.</p><p> Identified factual errors, logical gaps, hallucinations, weak reasoning, and incomplete answers in model outputs, then delivered structured feedback used to improve model quality.</p>