I am a Data Scientist with a strong passion for Machine Learning (ML), Deep Learning, Natural Language Processing (NLP), Graph Neural Networks (GNNs), and Large Language Models (LLMs). My goal is to develop practical AI solutions that drive real-world impact.

My experience includes:

• Custom fine-tuning of LLMs ( LLaMA, Mistral) for enterprise applications.
• Design and deployment of high-performance graph-based retrieval systems.
• Advanced AI model optimization for GPU acceleration and distributed computing.
• End-to-end AI pipeline automation and cloud-based deployment strategies.
• Production-ready AI architectures with robust API integration and MLOps best practices.

I enjoy collaborating on challenging projects and continuously learning about advancements in AI and machine learning. I am passionate about driving innovation through AI and leveraging state-of-the-art models to solve complex industry challenges.

• Master’s in Data Science
  Higher Institute of Information and Communication Technologies (ISTIC)
  (2020 – 2022)

  • Specialization in Data Science & Smart Services.
  • Research on Graph Neural Networks (GNNs) for anomaly detection.

• Bachelor’s in Computer Science & Communication
  Higher Institute of Information and Communication Technologies (ISTIC)
  (2017 – 2020)

  • Focus on Data Structures, Algorithms, and AI-based applications.

✅ LLMs & NLP: Development of custom GPT models, fine-tuning Transformer architectures, optimization for low-latency inference.

✅ Graph AI & GNNs: Knowledge graph construction, GNN-based semantic search, AI-driven recommendation systems.

✅ Machine Learning & Deep Learning: Advanced ensemble learning, CNNs, YOLO, Bayesian ML, XGBoost.

✅ Data Engineering & Databases: Real-time AI pipelines, NoSQL & SQL databases, stream processing with Kafka.

✅ Cloud & DevOps: High-performance computing (HPC) for AI workloads, model compression, multi-cloud strategies.

• Architected and deployed scalable, production-ready AI models using FastAPI & Flask.
• Engineered state-of-the-art LLM pipelines, integrating LangChain & Elasticsearch to enhance retrieval-augmented generation (RAG).
• Designed graph-based retrieval systems that reduced search latency by 20%.
• Optimized GPU utilization on AWS & OVH Cloud, cutting model inference time by 25%.
• Built AI agents using LangGraph, improving automation efficiency by 30%.
• Implemented multi-cloud AI deployment strategies, reducing operational costs by 10%.

• Developed BERT-based NLP pipelines for text classification and document analysis.
• Integrated Graph Neural Networks (GNNs) into AI-driven knowledge extraction systems.
• Built custom AI models for semantic similarity detection, boosting accuracy by 15%.
• Designed automated data ingestion pipelines for large-scale text datasets.
• Applied LLM embeddings for enhanced information retrieval and ranking systems.

• Designed a GNN-based anomaly detection framework, improving detection rates.
• Applied supervised learning techniques to enhance low-resource AI model performance.
• Optimized edge-node embeddings in graph networks for anomaly classification.

📜 AI Analyst – IBM
📜 Big Data Engineer – IBM
📜 Machine Learning & AI Specialization – Kaggle

🚀 Efficient Fine-Tuning Strategies – PEFT, Quantization, LoRA
🚀 Advanced Graph AI – Heterogeneous GNNs, GNNs for Fraud Detection
🚀 Real-time AI & Edge Computing – AI models optimized for IoT & Embedded AI

📧 Email: chaymaelbahry1999@gmail.com
💼 LinkedIn: Chayma Elbahri