class YassineGhilani:
def __init__(self):
self.name = "Yassine Ghilani"
self.role = "AI Engineer & Full-Stack Developer"
self.location = "Tunisia 🇹🇳"
self.education = "BSc Computer Science — ISITCOM Sousse"
self.website = "https://ghilaniyassine.tech"
@property
def focus_areas(self):
return [
"🤖 Large Language Models (LLMs)",
"📚 Retrieval-Augmented Generation (RAG)",
"🕸️ Agent Orchestration & Multi-Agent Systems",
"🌐 NLP Applications & Semantic Search",
"☁️ Azure AI & Cloud-Native Solutions",
"⚙️ Backend APIs with Django & FastAPI",
]
@property
def current_mission(self):
return "Turning cutting-edge AI research into real-world impact."
def fun_fact(self):
return "When not training models, I'm training on the 🏀 basketball court!"
| Domain | Skills |
|---|---|
| 🤖 Generative AI | LLM Fine-tuning, Prompt Engineering, RLHF, RAG Pipelines |
| 🕸️ Agent Systems | Multi-Agent Orchestration, Tool Use, Memory Management |
| 📚 NLP | Semantic Search, Text Classification, Named Entity Recognition |
| ☁️ Azure AI | OpenAI Service, Cognitive Services, Azure ML, AI Search |
| ⚙️ Backend | RESTful APIs, Microservices, WebSockets, Async Pipelines |
| 🚀 Deployment | Docker, CI/CD, Cloud-Native, Scalable Architectures |
graph LR
A[🧠 Raw Data] -->|Ingestion| B[📄 Chunking & Embedding]
B -->|Store| C[🗄️ Vector DB - ChromaDB]
C -->|Retrieve| D[🔍 Semantic Search]
D -->|Augment| E[🤖 LLM - GPT / Azure OpenAI]
E -->|Generate| F[✅ Intelligent Response]
F -->|Serve| G[🌐 FastAPI / Django API]
Building production-grade RAG systems, intelligent agents, and NLP pipelines that bridge research and real-world value.
- 🏅 Microsoft Azure AI — Azure AI Services & Solutions
- 🤖 LLM Engineering — RAG, Fine-Tuning & Agent Orchestration
- 📊 ML & Deep Learning — PyTorch, TensorFlow pipelines
- 🌐 Backend Mastery — Django, FastAPI, REST API design
I'm open to collaborating on AI research, LLM applications, RAG systems, and open-source NLP projects.
✨ Always curious. Always building. Always learning. ✨