DATA ENGINEERING · PYTHON BACKEND · AI/ML

Mouad Elbaz

Junior Engineer · Data, Python Backend & AI/ML

I build data pipelines, Python services, and reproducible machine-learning systems.

From enterprise ELT and FastAPI services to retrieval systems and model training, I turn complex data into useful, testable applications.

Portrait of Mouad Elbaz
200,000+enterprise records integrated
16,357medical QA records cleaned
38plant disease categories

Case studies

Featured Projects

Selected work across enterprise data, Python services, and AI research.

01

Data Engineering · Backend · AI

PFE · Professional project

Operational Intelligence Platform

Enterprise ELT, RAG, and ML dashboard

A data and AI platform developed during my PFE internship to consolidate, process, analyze, and query enterprise IT operations data.

  • Consolidated 200,000+ records
  • Sources included GLPI ITSM, OCS Inventory, and security logs
  • Asynchronous ELT pipeline
AirflowdbtQdrantBM25XGBoost
Implementation details — Operational Intelligence Platform
  • Medallion Architecture
  • Bronze / Silver / Gold analytical layers
  • Apache Airflow orchestration
  • dbt transformations
  • Multilingual RAG system
  • BM25 + dense vector retrieval
  • Reciprocal Rank Fusion
  • Cross-Encoder reranking
  • Qdrant
  • XGBoost ticket triage
  • Isolation Forest hardware anomaly detection
  • FastAPI backend
  • Next.js KPI dashboard
Isolation ForestFastAPINext.js
GitHub
real.screenshots9 views

Dashboard, analytics, ELT, RAG, and operational intelligence views.

02

Data Engineering · Backend · AI

CCSR — Canonical Computer Science Research

Reliable ingestion, retrieval, and research workflows

An open-source, local-first AI research engineering platform evolved from RAGForge. CCSR is designed around reproducible experimentation, evaluation, artifact lineage, retrieval tracing, and research-oriented AI workflows. Its current mature domain is Retrieval-Augmented Generation.

  • Durable Bronze / Silver / Gold ingestion using MinIO
  • PostgreSQL metadata, lineage, and authoritative state
  • Dense + BM25 sparse hybrid retrieval with Qdrant
FastAPINext.jsPostgreSQLQdrantMinIO
Implementation details — CCSR — Canonical Computer Science Research
  • Configurable chunking strategies
  • Retrieval tracing and source inspection
  • SSE streaming
  • Redis caching and ingestion events
  • FastAPI control plane
  • Next.js frontend
  • Airflow and Celery ingestion orchestration
  • Benchmarking for latency, throughput, retries, recovery, duplicate processing, and scaling behavior
RedisAirflowCeleryDockerBM25FastEmbed
GitHub
System overviewLocal-first RAG
01Documents
02Durable ingestion
03Retrieval + tracing

CCSR — Canonical Computer Science Research

03

AI & Machine Learning

Research in progress

Qwen MedQuAD — Medical LLM Fine-Tuning Research

Medical QA preparation and QLoRA fine-tuning

A reproducible research project studying when medical language models should rely on domain fine-tuning, retrieval, or calibrated abstention. The project uses MedQuAD and Qwen to investigate answer quality, grounding, uncertainty, and robustness.

  • Built a leakage-audited MedQuAD cleaning pipeline
  • 16,357 cleaned QA records
  • Fine-tuned Qwen3-4B using 4-bit QLoRA
Qwen3QLoRAPyTorchTransformersPEFT
Implementation details — Qwen MedQuAD — Medical LLM Fine-Tuning Research
  • 14,344 normalized question groups
  • Group-safe train / validation / test split
  • No detected question-group leakage
MedQuADPython

Research design · Controlled 2×2 comparison

  • Base Qwen
  • Base Qwen + RAG
  • Fine-tuned Qwen
  • Fine-tuned Qwen + RAG

Planned: complete four-system evaluation, retrieval stress tests, final statistical analysis, and larger Qwen model experiments. Research topics include answer quality, evidence grounding, uncertainty, robustness, and selective answering / abstention.

GitHub
System overviewFine-tuning research
01MedQuAD data
02Group-safe split
03Qwen3-4B QLoRA

Four-system evaluation: planned

Qwen MedQuAD — Medical LLM Fine-Tuning Research

04

AI & Machine Learning

PlantGuard AI — Plant Disease Detection

Plant disease classification with OOD detection

A deep learning plant disease classification system combining supervised image classification with out-of-distribution detection.

  • CNN, ResNet, and EfficientNet-B3 benchmarking
  • 38 disease categories
  • 70,000+ PlantVillage images
PyTorchTorchVisionEfficientNet-B3ResNetCNN
Implementation details — PlantGuard AI — Plant Disease Detection
  • Approximately 99.7% F1-score
  • MSP out-of-distribution detection
  • GOAD out-of-distribution detection
  • Streamlit deployment
  • Dockerized application
GOADStreamlitDocker
GitHub
real.screenshots1 view

Plant disease detection interface and model preview.

Experience

Professional Experience

Apr 2026 – Jul 2026

Data Engineering & Machine Learning Intern (PFE)

Prefecture d'Agadir Ida-Outanane · Agadir, Morocco

Integrated 200,000+ enterprise records with asynchronous ELT, Airflow, and dbt; built multilingual retrieval and ML services in FastAPI for a Next.js dashboard.

Medallion ArchitectureAirflowdbtRAGXGBoostIsolation ForestFastAPINext.js

Apr 2025 – Jun 2025

Developer Intern

Prefecture d'Agadir Ida-Outanane · Agadir, Morocco

Built an enterprise attendance-management system with RBAC, authentication, session management, IP compliance checks, and audit logging.

RBACAuthenticationAudit LoggingNetwork Security

Capabilities

Skills in practice

Explore the tools through the projects where I have used them.

Data Engineering

  • Python
  • SQL
  • Apache Airflow
  • dbt
  • ETL / ELT
  • PostgreSQL
More tools

PySpark, Apache Spark, Hadoop, MinIO, Medallion Architecture

See related project

Python Backend

  • FastAPI
  • REST APIs
  • Celery
  • Redis
  • PostgreSQL
  • Docker
  • SSE
More tools

Java, Spring Boot, MySQL, authentication, audit logging

See related project

AI & Machine Learning

  • PyTorch
  • QLoRA
  • RAG
  • Scikit-learn
  • XGBoost
  • Model evaluation
More tools

Qwen3, BM25, Qdrant, FAISS, hybrid retrieval, Pandas, NumPy

See related project

Research

Research Interests

Interested in reproducible AI research, Retrieval-Augmented Generation, LLM fine-tuning, uncertainty estimation, selective prediction, multimodal AI, and explainable AI.

Current research direction

Brain Region-Aware Explainable Artificial Intelligence for EEG-Based Neurological Disorder Detection

Exploring how spatial brain-region information and temporal EEG features can improve both classification performance and interpretability.

Learning path

Education

1

Bachelor in Big Data

Universite Sultan Moulay Slimane · Morocco

2026

2

Technicien Specialise en Reseaux et Systemes

ISTA Agadir · Agadir, Morocco

2023 – 2025

3

Baccalaureat Sciences Physiques

Morocco

2022 – 2023

Certifications

Machine Learning Specialization — DeepLearning.AI
Network Security — Cisco
Developer & Cybersecurity Pathways — Microsoft Learn
Introduction to Data Warehousing — 365 Data Science

Languages

  • Arabic — Native
  • French — Intermediate
  • English — Advanced

Contact

Let’s work together

Start a conversation

Contact me about data engineering, Python backend, or AI/ML opportunities.

Location: Agadir, Morocco

Phone: +212 751 81 12 01

Email: elbaz.ouad1249@gmail.com

GitHub: moad-cod

Email me directly

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