Jawher Ennaceur
AI Engineer
5+ years exp
5+ projects
C
Python
JavaScript
Java
C++
Active 6 days ago
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Overview
Technical skills
Timeline
Roles
Overview
AI Engineer focused on applied NLP and knowledge-based assistants. Has internship experience building LLM-based extraction and automated feedback systems, plus work on e-commerce purchase-intent modeling. Also works with RAG-style chat assistants and data-driven fraud detection projects.
Technical skills
C• Middle • 5y+ • 4 projects
Python• Middle • 4y+ • 5+ projects
JavaScript• Junior • 4y+ • 4 projects
Java• Junior • 4y+ • 4 projects
C++• Junior • 4y+ • 4 projects
Databases
PostgreSQL• 4y+ • 5+ projects
MySQL• 4y+ • 4 projects
Chroma• 4 projects
FAISS• 2 projects
Neo4j• 2 projects
AI/ML
Prompt Engineering
RAG
LLM
Transfer Learning
DevOps
Azure
Docker
GCP
Timeline
Institut Supérieur des Sciences Appliquées et de Technologie de Sousse (ISSAT)
Master's Degree •
Software Engineering
AI Internship
•
Junior
Data Scale
•
Full-Time
Worked on an e-commerce purchase-intent prediction pipeline using low-cardinality categorical features across shared and client-specific data. Built a workflow that fine-tunes models per client using site-specific data to simplify onboarding for new e-commerce customers. Also supported building a knowledge base and API for service booking to improve customer support operations.
Transfer Learning
AI Engineer Intern
•
Junior
Class Quiz
•
Full-Time
Designed an automated system to structure exam content and handle student responses, including blank tests and handwritten answers. Implemented functionality to generate feedback automatically to reduce manual grading effort and standardize feedback for teachers. Integrated conversational capabilities with a speech-to-text/text-to-speech layer around an existing chatbot.
AI Engineer Intern
•
Junior
Deloitte
•
Full-Time
Built AI agent workflows for business-logic decisioning with a focus on reliability and traceability. Developed a knowledge graph pipeline from unstructured documents, including semantic segmentation and LLM-based structured extraction. Compared different model approaches to evaluate performance for extraction/classification tasks.
LLM