I build systems where agents and language models reason in a way that can be checked: LLM output turned into formal, verifiable structures, and agents that negotiate under explicit protocols.
Looking for a 6-month end-of-studies internship from early February 2027 in agentic AI, LLMs or multi-agent systems, Paris / Île-de-France. Ranked 1st of my MSc year-1 cohort, highest honours.
LinkedIn · mohammedinoredine@gmail.com
Neuro-symbolic GORGIAS extraction · personal project
Turns French argumentative text into an LPP/GORGIAS logic program. A local LLM (Ollama) only answers closed questions; spaCy and deterministic rules decide the rest. Hand-annotated benchmark with negative examples, 97.9% rule precision, 171 tests.
Python Ollama spaCy neuro-symbolic AI
LPP Argument Mining · MSc research project in a pair, ANR GRAIL
LLM annotation prompt producing LPP structures in brat format, and a Python/Graphviz pipeline that draws the argumentation graphs. With Thomas Gourmelen.
Python LLM prompt engineering Graphviz
TrainMind · multi-agent negotiation
Four JADE agents negotiate a safe training session over FIPA-ACL with an iterative Contract Net.
Java JADE FIPA-ACL Contract Net
Abstract argumentation solver · Dung frameworks, preferred and stable semantics, CLI and tests. Python
Information retrieval engine · TF-IDF, BM25+, SentenceTransformers, Recall/Precision/MRR (Kaggle). Python
TCF Prep · microservices on Kubernetes with Istio mTLS. Node.js React PostgreSQL Docker
AI: Python, PyTorch, Hugging Face, LangChain, scikit-learn, spaCy, Ollama · Agents: JADE, FIPA-ACL, Google ADK · Engineering: Java, TypeScript, FastAPI, Django, React, PostgreSQL, Docker, Kubernetes, Git, Linux
Evaluating LLM agents: building test sets with negative cases before changing a prompt or a model.