Freelancer profile translated to English.
Back to original languageAbout Clément
Generative AI Project Manager with a background in Data Science. I manage complex projects for major clients, from roadmap to production deployment. My technical expertise allows me to anticipate risks, make the right architectural choices, and bridge the gap between business and tech. Engineering degree from ENSAE & MVA (ENS Cachan).
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What I do for you:
• AI Scoping & Prioritization (audit of existing systems, clear ROI in 2 weeks)
• Roadmap & Governance (processes, KPIs)
• AI/GenAI Project Management (RAG, agents, Data Capture, etc.)
• Coordination of industrialization and production deployment (Azure/Cloud/K8s, CI/CD, monitoring, Responsible AI)
• Change & Adoption (workshops, training, business onboarding)
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What sets me apart:
• A true technical background: Data Science engineering cycle (ENSAE) + Master's in Data Science research at ENS (MVA) + initial years as a Data Scientist. I design technically sound solutions and anticipate business needs.
• 5 years of consulting experience: accustomed to quickly onboarding into new environments and delivering in complex organizations (banking, luxury).
• Proven management skills: I created and grew an NLP/GenAI expertise hub to 25 people in a consulting firm.
French
Native or bilingual
English
Fluent
Can work on-site
Paris (up to 10km)
Experience
- CHANELAI & GenAI Project ManagerLUXURY GOODSJanuary 2024 - Today (2 years and 7 months)Neuilly-sur-Seine, FranceManaged about ten AI/GenAI projects within a strategic team of 25 people, from ideation to production deployment, with management reporting.• Led the development of an internal GenAI application designed for group-wide deployment (~10,000 employees) and production on Azure cloud, interfacing between technical and business teams.• Designed a comprehensive methodology for evaluating RAG systems (RAGAS, DeepEval, Grouse, MS Foundry benchmarks) and an observability framework for AI agents in production (Langfuse, LangSmith, MS Foundry benchmarks).• AI Transformation Program: multi-division strategic scoping; workshops for gathering and prioritizing use cases, exploring technical solutions (MS Foundry, Claude stack, Copilot Studio), deployment roadmap, and long-term vision document for management.• Multi-label image classification within Digital Asset Management, ~90% average accuracy.• Budget monitoring and international collaboration (daily English).
- BNPAI Project ManagerBANKING AND INSURANCEJuly 2022 - January 2024 (1 year and 6 months)Paris, FranceManaged end-to-end AI projects (design → go-live) and defined technical strategy (algorithm choices, evaluation frameworks), while maintaining technical involvement (Python Data Science development ~50% of code, Docker).• Extracted financial data from heterogeneous scanned documents under strict constraints (on-premise, CPU-only, no multimodal LLM): designed and coded the pipeline logic (OCR → table detection → reconciliation by accounting formulas), scoped business needs, coordinated dataset annotation, and defined the scope. Target reliability ~98%.• Gathered business requirements and wrote technical, functional, and architectural specifications (business/IT/CISO workshops).• Coordinated deployment in close liaison with IT and security teams (CISO), managed performance tests and acceptance.• Designed and delivered an LLM training session delivered in an auditorium, filmed and integrated into the company's official training library.• Collaborated with international teams, daily work in English.
- BNPAI Engineer / Data ScientistBANKING AND INSURANCEJanuary 2021 - July 2022 (1 year and 6 months)Paris, FranceDeveloped and deployed AI backends in Python (algorithms, models, APIs) on on-premise infrastructure, Docker / Kubernetes / Jenkins CI/CD.• Hybrid search engine indexing ~100,000 documents for 2,000 users across multiple SharePoints: lexical and semantic search (embeddings), response time ~2 s. Dockerized FastAPI API, daily incremental indexing via Elasticsearch.• Virtual assistant for answering internal questions: semantic retrieval (embeddings) on a FAQ knowledge base (Python, FastAPI, Angular).• Improved classification rules base: analyzed a large repository of manually defined settings to detect abnormal, duplicate, or non-compliant rules.
Reviews
Recommendations
+6
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Education
- MSc in Mathematics, Vision and Learning (MVA)Ecole Normale Supérieure (ENS)2020Intelligence Artificielle, Data Science, Machine Learning, Reinforcement Learning, NLP, Computer Vision, Kernel Methods, Mathématiques fondamentales
- Engineering Degree - MSc Data ScienceENSAE2020Data Science, Machine Learning, Reinforcement Learning, NLP, Computer Vision, Mathématiques fondamentales, Statistiques
Certifications
- Microsoft Certified - Azure FundamentalsMicrosoft2024
- Prince2 PractitionerPeopleCert2025