Freelancer profile translated to English.
Back to original languageAbout Nael
AI & Machine Learning Engineer (Stanford) | GenAI & Fullstack Expert
Need to integrate cutting-edge AI capabilities or deploy complex models? I help companies create intelligent solutions, from research (R&D) to scale-up deployment.
What I bring to you concretely:
- Custom RAG & LLM Systems: Creation of document ingestion pipelines (OCR, semantic chunking) and integration of local models (Gemma, Qwen) with a proven retrieval accuracy of 92%.
- Advanced Deep Learning Models: Expertise in diffusion models, Computer Vision, and Reinforcement Learning to solve complex problems.
- Infrastructure & Deployment: As a former Founding Engineer, I don't just deliver experimental code. I build robust and secure APIs with FastAPI/Flask and Docker, ready to process thousands of documents.
- Scientific & Physics AI: Thanks to my master's degree at Stanford, I can apply Machine Learning to physical simulation and mechanical engineering problems (Physics-based ML).
Why choose me?
My hybrid profile allows me to understand your business needs as well as the deep technical constraints. I have already proven the value of my solutions in startup (Cirkles.ai) and industrial (Thales) environments, with a constant focus on performance (PSNR +50% compared to standard baselines).
Typical deliverables:
- Backend & ML Serving Architectures (FastAPI, Vector Databases).
- Full-stack analyst interfaces for AI-assisted decision-making.
- Anomaly and fraud detection models.
- Volumetric and scientific data processing pipelines.
Let's discuss your project to transform your data into competitive advantages.
My website link:
French
Native or bilingual
English
Native or bilingual
Can work on-site
Paris (up to 50km)
Experience
- Cirkles.aiFounding Software Engineer (Core Team)CONSULTING AND AUDITSJuly 2025 - Today (11 months)Paris, France• · Co-built an ML-driven SaaS platform (anomaly & fraud detection on financial documents) as a founding engineer on a 3-person team, processing over 5000 documents during the pilot phase.• · Architected the backend with FastAPI, building secure APIs for data ingestion, real-time inference, and ML model serving.• · Developed anomaly detection models and a full-stack analyst UI, automating the decision-making workflow end-to-end.
- ThalesData Scientist - InternDEFENSE AND MILITARYJune 2025 - September 2025 (3 months)Paris, France• · Developed DI2C, an internal RAG system to query technical documentation using local LLMs (Ollama, Gemma, Qwen).• · Built a Flask + Streamlit ingestion pipeline with OCR and semantic chunking, indexing PDFs into a Weaviate vector store with 92% retrieval accuracy.• · Engineered a hybrid retrieval pipeline (SentenceTransformers) optimizing context prioritization to minimize hallucinations.
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Education
- Master of Science in Mechanical EngineeringStanford University2026Ingénierie mécanique, Data Science, Physiques, Statistiques, Mathématiques, IA
- Arts et Métiers EngineerÉcole Nationale Supérieure d'Arts et Métiers2026Génie mécanique et industriel