
Hifza
AI Engineer, Generative AI and RAG Systems Specialist
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Work experience
AI Engineer (Independent Project) — RAG & Data Extraction Systems
GitHub • Self-employed
May 2026 - Present • 5 mos
Built an end-to-end enterprise Retrieval-Augmented Generation (RAG) system (TechCorp Financial RAG) that processes large-scale financial documents and structured data. Key highlights: - Designed a PDF data-extraction pipeline that parsed 250 SEC 10-K financial filings (28,862 pages) into 131,209 clean, source-traceable text chunks - Built a semantic search layer using vector embeddings (FAISS, ChromaDB) to enable accurate retrieval from unstructured documents - Developed a hybrid query engine that routes natural-language questions between a structured SQL database (1M+ rows) and unstructured document search - Implemented a natural-language-to-SQL agent with schema-aware prompting and security guardrails against destructive queries - Optimized database performance using B-Tree indexing, reducing query time by over 99% on large datasets - Deployed a Streamlit web dashboard with a live chat interface and source-traceability panel Tech stack: Python, LangChain, PyMuPDF, FAISS, ChromaDB, SQLite, Streamlit, FastAPI, Google Gemini 2.5 Flash, HuggingFace Embeddings Self-taught AI/ML engineer (BS IT, 8th semester) with hands-on, production-style project experience in data parsing, automation, and AI-driven retrieval systems.