Pierre Kasparian, GDPR-compliant AI integration consultant
Fourth-year engineering student at UTT (Université de Technologie de Troyes), specialising in computer science and information systems, I have been working as a freelancer since 2024 on AI integration and data engineering projects for French and European SMBs and startups.
After a six-month internship at Pretto (French fintech, mortgage lending) followed by three months of freelance, I have hands-on production LLM experience: RAG pipelines, multi-provider batch inference services, AI automation, and prompt evaluation with Langfuse. I also worked with LiveSession on a multi-tenant RAG chatbot.
My positioning: AI integration with a non-negotiable GDPR compliance constraint. In practice, this means European hosting (OVH, Scaleway), self-hosted open source models (Mistral, LLaMA) for sensitive data by default, and skills transfer at the end of every engagement.
Background
Linear and logistic regression, SVM, k-means clustering, decision trees, dimensionality reduction (PCA). Implemented with scikit-learn and pandas. Capstone project: stock price prediction using regression on historical data.
Full RAG stack (chunking strategies, embedding, retrieval, cross-encoder reranking), QLoRA fine-tuning on Mistral and LLaMA, autonomous agents with LangChain and n8n, open source model deployment via Ollama. Udemy certification obtained in January 2025.
UTT - Computer Science & Information Systems
5-year engineering programme with a data specialisation. Modules: algorithms, relational databases, software architecture, software engineering, networks, security. Chosen 4th-year specialisation: data engineering and applied AI.
Experience
Pretto
Aug 2025 – Feb 2026AI & Data Intern
Built Python-Airflow-dbt ETL pipelines for production. Multi-provider LLM batch inference service (OpenAI, Anthropic, Mistral, Vertex AI) processing 3,000+ inputs per day. 50% inference cost reduction through dynamic model routing based on server load and token volume.
AI & Data Freelance
Prompt auto-improvement pipeline using Langfuse and annotated datasets. LLM platform hardening: removing base64 processing, centralising third-party clients behind a Factory pattern. Slack-native prompt evaluation platform refactoring, reducing production regressions by 80%.
AI & Data Freelancer
2024 – present10+ projects delivered since 2024: multi-tenant RAG chatbots, ML classification on business data (91% precision), AI automation with n8n, SaaS development with Next.js and FastAPI. Data engineering (SQL, dbt, Airflow) as foundation before any AI integration.
See all my worksJunior Conseil UTT
2023 – 2025Technical Director and Project Lead
Led client projects, managed technical teams and coordinated deliverables. Developed a web application for a solar safety association.
Head of Communications & Events
Managed social media, external communications and annual event organisation for the student association.
Public references
A few public mentions and projects that give context on my entrepreneurial, product and AI work.
My values
GDPR by design
Every integration is built for compliance from day one. In practice: hosting provider selection is the first constraint, not the last; self-hosted open source models by default for sensitive data; systematic CLOUD Act assessment before integrating any US API. Compliance is not a cost, it is a selling point.
Knowledge transfer
I transfer skills. At the end of an engagement, you should be able to maintain and evolve what we built together. Every deliverable includes technical documentation and a handover session with the team. If the developers do not understand how the system works, the engagement is not finished.
Pragmatism
Fast POC, short feedback loop, iterative delivery. No over-engineering. A pipeline that works in production beats a perfect architecture on paper. I use tools suited to the problem, not the most fashionable.