Chayma Rhaiem
AI Engineer & Software Engineer
Building production RAG systems and AI-integrated development workflows
AI Engineer specializing in AI-integrated development workflows and production RAG systems. I've spent the last year and a half building cloud-native applications that intelligently monitor systems, detect anomalies, and provide automated insights. My approach centers on making AI a core part of the development process rather than just an add-on feature.
About Me
AI Engineer specializing in production RAG systems and AI-integrated development workflows with expertise in machine learning, cloud computing, and intelligent system design.
AI/ML Engineering
RAG systems, NLP pipelines, and scalable ML architectures
Data Engineering
Cloud data pipelines, vector databases, and real-time processing
Software Engineering
Full-stack development, system design, and production deployment
Latest Publication
Sharing insights from production AI systems
From Chaos to Context: How We Tamed SAP Monitoring with a Graph RAG Assistant
A complete implementation guide for building AI-powered SAP monitoring systems using Graph RAG architecture. Learn how we combined Neo4j knowledge graphs with vector search to create an intelligent assistant that processes SAP T-codes, detects anomalies, and provides contextual solutions in real-time.
Professional Experience
My journey in AI engineering and software development
Freelance Full-Stack Developer
B2B Dispatching Platform
- Designed and deployed cloud-based architecture using Firebase and websockets for real-time trip management
- Built intelligent dispatching system leveraging spatial optimization algorithms for driver assignment under spatio-temporal constraints
- Developed Flutter mobile app and web dashboard for GPS tracking, live monitoring, and operational analytics / Reporting
AI Engineer – Client in Logistics & Communication Sector, Europe
Avaxia Group
- Built cloud-based service with FastAPI to monitor SAP systems using T-codes, detect anomalies and critical errors, and suggest fixes from SAP help docs, SAP notes, and expert knowledge
- Built hybrid RAG architecture combining Knowledge graphs and vector databases for automated root-cause analysis (RCA).
- Scaled systems capacity by 70× while keeping response times under one second using Neo4j graph queries and Qdrant vector search
Key Projects
Enterprise SAP Monitoring System
Production-ready AI-powered monitoring system with 70× capacity increase and sub-second response times using hybrid Graph-Vector RAG architecture.
VIZIO Consulting Inc
- Delivered production RAG-based enterprise AI assistant using AWS Bedrock, S3 and Kendra enterprise search, demonstrating 25% internal efficiency gains
- Built real-time BI dashboards in SAP Analytics Cloud, PowerBI, and QuickSight processing large-scale data streams
- Optimized multi-cloud ETL architectures achieving 25% performance improvement in production reporting systems
Key Projects
AWS Glue to Power BI Pipeline
Built comprehensive ETL pipeline using AWS Glue for data transformation and Power BI integration, enabling real-time dashboard analytics for enterprise clients with automated data quality checks.
Bedrock Streamlit Chatbot with Claude Foundation Model
Pioneered early RAG implementation using AWS Bedrock's foundation models for intelligent document processing. Built when RAG was just emerging as a paradigm, featuring advanced prompt engineering and context-aware responses.
AWS S3 and Redshift Data Pipeline Optimization in Glue
Developed proof-of-concept AI assistant for major gas company operations, integrating multiple AWS services for document analysis, query processing, and automated response generation.
- Deployed credit risk scoring models for African banking sector with Basel II and IFRS 9 compliance requirements
- Achieved an additional 10% model accuracy improvement through AutoML frameworks and hyperparameter optimization
- Built production model validation ensuring regulatory compliance in live financial systems
Education
Academic foundation in software engineering and data science
Master of Science in Engineering (MSE) - Software Engineering & Data Science
ESPRIT (Higher School of Engineering and Technology)
Key Achievements
- CTI & EUR-ACE Accredited Program
- Awarded Magna Cum Laude (High Honors)
Relevant Coursework
Bachelor of Science in Computer Science - Software Engineering and Information Systems
Polytech-Intl
Key Achievements
- Strong foundation in computer science fundamentals
- Focus on software development and system design
Relevant Coursework
Certifications
Professional certifications in data science, cloud computing, and machine learning
Microsoft Certified: Azure Data Scientist Associate
Microsoft
Natural Language Processing Specialization
DeepLearning.AI
Building Transformer-Based NLP Applications
NVIDIA
Hedera Hashgraph Developer Certification
The Hashgraph Association
SAP Certified Application Associate - SAP Analytics Cloud
SAP
Google Data Analytics Specialization
Technical Skills
Expertise across the full AI engineering and software development stack
Machine Learning
Programming
Web Scraping & Tools
Cloud & Data
Featured Projects
Academic research and professional implementations
Professional Projects
Hybrid Graph-Vector RAG System
Hybrid RAG architecture combining graph and vector retrieval, Custom G-Eval framework for LLM response quality assessment, 70% alert accuracy matching expert-defined priorities, Conversational interface for natural language SAP queries, Automated RCA (root cause analysis) with contextual documentation
Credit Risk Assessment System
Automated credit PD (Probability of default) models under Basel II and IFRS 9 guidelines using 300,000+ customer records from African banks.
Academic Projects
Smart PM Hub – AI-Enhanced Project Assistant
Live RAG-powered project management platform combining Neo4j knowledge graphs with LLM reasoning. Built production NLP pipeline processing 500+ Reddit discussions and PMBOK 7 documentation with optimized 15s response time. Features real-time KPI visualization dashboard with graph-based analytics serving live project data.
Technologies Used
Innovest – AI Trading Platform
Real-time trading system for Tunisian markets with production trading system featuring real-time sentiment analysis processing 200+ daily financial articles. Integrated generative AI document processing streamlining automated workflow analysis. Built scalable cloud architecture for live market signal processing and storage.
Technologies Used
Object Detection Using Adaptive Mask RCNN in Optical Remote Sensing Images
This project presents a comparative study of three deep learning approaches for classifying images in the CIFAR-10 dataset: A basic Artificial Neural Network (ANN), A custom Convolutional Neural Network (CNN), Transfer Learning using VGG19 and ResNet-50.