Technical résumé
My profile does not come from one isolated specialty. It comes from moving continuously through the layers of a system — from code to operating systems, from data to intelligence, and into the physical world.
Each step added a new way to understand, build and connect technology.
Before software, I learned precision, material constraints and the logic of physical systems. That foundation still shapes how I approach hardware, electronics and prototyping.
Web development taught me to turn intent into a complete application: interface, domain logic, APIs, data, deployment and maintenance.
Operating the environments that run software shifted my attention from isolated code to systems: servers, containers, automation, availability and deployment.
Data Analysis taught me to frame a question, make sources reliable and turn data into useful information for decisions.
Data Science added experimentation, modeling and measurement: understanding signals, testing hypotheses and evaluating what actually works.
Data Engineering taught me that intelligence first depends on reliable pipelines, coherent models, measurable quality and architecture that can evolve.
Working around PLCs and EcoStruxure Automation Expert gave me a practical understanding of industrial constraints, real time and integration between software, data and machines.
Today I connect these layers to design Data & AI architectures — from problem and abstraction to prototype, measurement and deployable system.
A quick view of the contexts where these capabilities were put into practice.
2024 — 2026
Power BI dashboards delivering +15% efficiency in project monitoring; ML, RL, GAN and LLM prototypes integrated into Automation Expert; Linux Cloud/Edge environments and PLC benchmarks.
06/2024 — 07/2024
AI-assisted CV database into SharePoint, PHP/JavaScript/Python web collection and commercial dashboards.
2022 — 2023
Client sites and tools, a SuiteCRM portal through REST APIs and migration to dedicated IONOS servers.
Technologies change. These capabilities remain.
Turn a complex need into architecture, components, interfaces and a delivery path.
Develop applications, backends, APIs and business tools around clear, maintainable logic.
Collect, clean, model, move and expose data for analysis or AI.
Prototype and integrate ML, LLM and RAG with criteria for measurement, control and usefulness.
Deploy and understand Linux environments, containers, servers and Cloud/Edge architectures.
Understand PLC, OT, electronics and hardware constraints to integrate software with the real world.
Not a collection of badges: tools encountered through projects, prototypes and professional environments.
I use the Apple ecosystem extensively, both as my everyday working environment and as a design space.
The Mac sits at the center of my development workflow. I also design my own solutions for iPhone, Apple TV and Apple Watch, taking into account the constraints, uses and interaction modes specific to each device.
The content of those applications remains private. What matters here is the ability to think through coherent experiences across formats — from mobile, to the living room, to the wrist.
What this journey produced