Skills
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AI Engineering
- Agent Orchestration : LangChain • LlamaIndex
- LLM Inference : HuggingFace • Ollama
- Vector Database : Chroma DB
- RAG systems
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Machine Learning
- Predictive Modeling : Scikit-learn • XGBoost • Pytorch
- Reinforcement Learning : Stable-Baselines3
- Anomaly Detection
- Evolutionary Optimisation
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Software Engineering
- Programming : Python
- API : FastAPI • Flask
- Databases : PostgreSQL • DuckDB • SQLAlchemy
- Agentic Coding : Claude Code
- DevOps : Git • Github Actions • Docker • Pytest • uv • ruff • pyright
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Data Engineering
- Data Warehouse : Snowflake
- Data Transformation : dbt
- ETL Pipeline : Airbyte
- Data Orchestration : Airflow
- Cloud : Azure
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Data Analytics
- Data Cleaning : Numpy • Pandas • Polars
- Process Monitoring : Grafana • Prometheus
- Data Visualisation : Tableau • Metabase • Streamlit • Plotly
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Other
- Linux / Bash
- Arduino
- Markdown
- Latex
- Agile project management (Jira)
Work Experience
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AI Engineer
- Building an automated system combining web scraping and AI vision to digitize and process administrative tasks.
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Career Transition
- Comprehensive preparation for a career in AI, with focused training in agent orchestration, LLM inference and RAG systems.
- Developed a project portfolio showcasing AI agents, ML models deployment and RAG systems.
- 20 volunteer work experiences in 8 different countries.
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Marie Curie Research Fellow
- Awarded a competitive €245,000 grant to develop machine learning solutions for advanced manufacturing optimisation.
- Designed and implemented an anomaly detection system using ML models on sensor time-series data, resulting in a ~15% reduction in unplanned downtime.
- Developed a reinforcement learning framework to train robotic manipulators on precision reaching tasks.
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Postdoctoral Research Fellow
- Optimised composite structures via computational methods
- Applied topology and evolutionary optimisation to define optimal material layouts, achieving a 20% improvement in stiffness-to-weight ratio.
- Developed high-fidelity numerical models (FEA) to predict complex mechanical behavior, validating against experimental data.
- Employed machine learning on simulation models to identify optimal material parameters.
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PhD Researcher
- Developed passive vibration damping solutions for lightweight sandwich structures
- Performed modal analysis on aircraft structures using finite element analysis (Ansys).
- Implemented evolutionary optimization algorithms in Matlab to identify optimal damping configurations.
- Validated models experimentally using accelerometers, laser vibrometers and spectrum analysers.
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Teaching Assistant
- Modules taught : solid mechanics, computational engineering, Computer-Aided Design (CAD)
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Research Assistant
- Modelling of wear using adaptive meshing in finite element analysis applied to aerofoil blade friction damper in a Rolls-Royce gas turbine.
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Project Management Intern
- Harmonisation of CatiaV5 settings configuration for A350 programme across all the main Airbus plants (France, Spain, Germany, United Kingdom).
Education
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PhD in vibration mechanics and optimisation
- evolutionary optimisation
- exploratory data analysis
- data visualisation
- numerical modelling (finite element analysis)
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MSc Mechanical Engineering
- mechanical engineering
- computer science
- engineering mathematics
- electronics
- material science
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MSc Engineering System Management
- managing projects and programmes
- system modelling
- supply chain management
- sustainable engineering
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French 'classes préparatoires' (BSc Engineering)
- mathematics
- physics
- chemistry
Languages
| French | Native speaker |
| English | Fluent |
| Spanish | Proficient |
Interests
- Technology: electronics (Arduino, Raspberry Pi), Kaggle competitions
- Outdoors: trekking, slow travel, permaculture, geocaching, volunteering
- Hobbies: badminton (15 years), guitar (10 years), chess, yoga, meditation