Abdelhamid Assadi
Ai Engineer
Berlin
Summary
Engineering student with international experience in AI projects and research. Worked on practical solutions in data analysis, automation, and problem-solving across different fields. Comfortable working in teams and adapting to new challenges. Looking to contribute to projects where technology supports real needs.
Overview
1
1
year of professional experience
3
3
Certifications
4
4
Languages
Work History
Artificial Intelligence engineer
Räppe Smile
12.2024 - 07.2025
- Built automated data pipelines with LLM APIs and RAG.
- Generated medical sales datasets, stored in Amazon DocumentDB.
- Developed time series models in PyTorch and SageMaker.
- Improved forecasting accuracy by 1520% over baseline.
- Delivered production-ready code and optimized performance.
Artificial intelligence Engineering intern
APAIA Technology
06.2024 - 09.2024
- Built CTI-Bench to benchmark large language models :
- Optimized evaluation pipelines, cutting time by 12%.
- Automated CVE-to-CWE mapping with Python and deep learning.
- Created RAG pipelines with LangChain, saving 30% prep time.
- Applied NLP tasks for classification, search, and summaries.
Education
Master of Science - Informatics
Hochschule Schmalkalden
Schmalkalden, Germany 04.2001 -
Master of Science - Artificial Intelligence
Tekup University
Tunisia 04.2001 -
Preengineering Cycle - Pre-engineering Studies
Prepatory Institute of Monastir
Monastir,Tunisia 04.2001 -
Skills
Machine learning
Data science
Python programming
Time Series Forecasting
Statistical modeling
Computer Vision
Natural Language Processing (NLP)
Large Language Models (LLMs) & Transformers
Retrieval-Augmented Generation (RAG) & LangChain
AWS (SageMaker, DocumentDB)
Data Pipelines
Big data technologies
Projects
Real-time Garbage Detection System (GitHub Repository)
- Built real-time garbage detection system using YOLOv8 and PyTorch
- Collected and labeled 1,500+ images to improve dataset quality
- Achieved 0.76 mAP@50 for people detection in street photos
- Reduced inference time to 25ms per image for real-time use
- Planned dataset expansion and testing larger Transformer-based models
Movie Recommendation System (GitHub Repository)
- Built hybrid recommender with content-based and collaborative filtering
- Applied RAG concepts, improving recommendation coverage by ~12%
- Designed interface for new user preferences and returning user history
- Scaled system to e-commerce and education with 1M+ interactions
- Set up pipeline with evaluation, boosting accuracy by 9%
Certification
AWS Certified Machine Learning Specialty
Timeline
Artificial Intelligence engineer
Räppe Smile
12.2024 - 07.2025
Artificial intelligence Engineering intern
APAIA Technology
06.2024 - 09.2024
Master of Science - Informatics
Hochschule Schmalkalden
04.2001 -
Master of Science - Artificial Intelligence
Tekup University
04.2001 -
Preengineering Cycle - Pre-engineering Studies
Prepatory Institute of Monastir
04.2001 -