Freelancer profile translated to English.
Back to original languageAbout Elian
I am an AI and Machine Learning engineer, specialized in NLP, Retrieval-Augmented Generation (RAG) and production model deployment. A graduate of CentraleSupélec with a specialization in Artificial Intelligence, I have designed, evaluated, and industrialized large-scale AI systems in an industrial environment.
What I do best:
Design end-to-end AI/NLP pipelines (RAG, LLM fine-tuning, seq2seq models)
Benchmarking parsing, chunking, retrieval, and evaluation strategies
Building scalable, tested, and production-ready ML systems (MLOps)
Optimizing model performance, latency, and reliability
Key experiences:
EDF – Industrialization of a large-scale RAG system, with test automation, code quality, and pipeline optimization
SNCF – Development and deployment of a complete FAQ generation pipeline and fine-tuning of LLMs for internal use cases
MICS Laboratory (CentraleSupélec) – Training a seq2seq Transformer model from scratch for system architecture prediction from logs
Technical stack:
Python, PyTorch, LangChain, RAGAS, Streamlit, Docker, APIs, pytest, CI/CD, prompt engineering, MLOps
If you are looking for someone capable of designing, evaluating, and deploying high-impact AI/NLP solutions, I would be delighted to assist you.
English
Native or bilingual
Spanish
Conversational
French
Native or bilingual
Can work on-site
Grenoble (up to 20km)
Experience
- EDFEND OF STUDY INTERNSHIP IN DATA SCIENCEENERGY AND UTILITIESApril 2025 - October 2025 (6 months)Grenoble, FranceDuring my end-of-study internship at EDF, I designed and industrialized a large-scale RAG system to assist in the writing of hazard studies. This included setting up an end-to-end evaluation pipeline for different parsing and chunking solutions (Docling, MinerU, VLM, similarity-based chunking, etc.) as well as benchmarking several retrieval strategies.I focused on production-level code quality, automated testing (pytest, Ragas), and pipeline optimization with Streamlit, LangChain, and API integrations.Through this work, I consolidated my expertise in MLOps and Deep Learning applied to industrial-scale language understanding.
- SNCF & ASK FOR THE MOONGAP YEAR INTERNSHIP IN DATA SCIENCETRANSPORTATIONJune 2023 - December 2023 (6 months)Paris, FranceDuring my gap year internship at SNCF, I worked on natural language processing solutions, including fine-tuning large language models and implementing a RAG-based question-answering system. I designed a complete FAQ generation pipeline, containerized with Docker and integrated into the internal environment, which strengthened my skills in model deployment and performance optimization.
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Education
- Graduate ofCentraleSupélec2025Diplôme ingénieur mention intelligence artificelle