Mohamed Aziz Allaya

Designed, implemented, and deployed CarbonComplianceAgent, a privacy-preserving agentic RAG system for EU CBAM carbon-compliance document analysis. Built a production-ready agentic RAG architecture using LangGraph, FastAPI, Streamlit, and Docker Compose, including a five-tool orchestrator, hierarchical document ingestion, and article/page-level citation traceability.

Built a diagnostic analysis solution to identify root causes of chatbot conversation failures from production logs. Analyzed production conversation logs using Python to detect recurring failure patterns and support continuous improvement of chatbot performance.

Analyzed financial data, market trends, and industry case studies to assess AI and data technology use cases in risk management, banking, and investment decision-making.

Developed a private internal web application store using the MERN stack, enabling employees to discover and launch internal tools from a single portal.

Built a customer-facing e-commerce website end-to-end using ASP.NET, covering front-end UI and back-end business logic.

Built a student enrollment portal using Mendix, digitizing a previously manual, paper-based registration workflow.

Pursuing an Engineering Degree in Computer Science with a Data Science track, including a graduation project on an agentic RAG system for CBAM carbon compliance.

Completed certifications in RAG LLM agents, transformer-based NLP, AI for anomaly detection, and fundamentals of deep learning.

Completed an introduction to large language models certification.

Completed a Hashgraph developer course.

Completed certifications in switching, routing, and wireless essentials.