Hello, I'm Michele.

Frontend engineer on a large-scale marketplace platform, embedded in a cross-functional team responsible for the seller-side experience.

• Contributed to the redesign and rebuild of the seller portal alongside two other frontend developers. Delivered new pages, asset management views and financial document integration leading to a 75% reduction in internal FinOps support tickets.
• Developed a custom AI-assisted workflow using dedicated agents to streamline TDD implementation, taking features from Jira tickets and Figma designs through to tested, reviewable code.
• Collaborated closely with multiple product teams (buyer, search, and others) as well as PMs and designers to ship cohesive, production-ready features.

Technologies: React.js, TypeScript, Next.js, CircleCI

Lead frontend developer on a six-person cross-functional team, building a web UI to visualise real-time data within a 3D digital twin of a building. Took the product from proof-of-concept to first customer release.
Solved key performance bottlenecks in 3D mesh rendering at scale and implemented live data streaming via WebSocket.

Technologies: React.js, TypeScript, THREE.js, Apollo/GraphQL, WebSocket, AWS

Joined directly after completing my Master's degree, gaining first professional exposure to full-stack web development in a small team environment. Contributed to two internal applications: a form builder and a data portal.

Technologies: React.js, Next.js, MongoDB, and GitLab CI/CD pipelines under the guidance of senior developers.

Developed a simple blog website with energy consumption reduction mindset, for a challenge sponsored by CGI at Junction 2022. Our team was composed by three people in total.
Implemented a clean and efficient UI using JavaScript and React.js to cut energy consumption to minimum.
Worked on profiling energy usage from CPU components to maximize energy efficiency.
The project placed second out of the 20+ teams that worked on the challenge.

120 ECTS in “Theory of Fundamental Interactions and Cosmology”

Thesis

Modelling electromagnetic emission from Kilonovae: photosphere and temperature profiles

Developed a predictive computational model to study Kilonovae emissions. The simulations have been coded in Python. Performed results validation using data analysis methods such as regression processes, chi squared tests and autocorrelation computations.

180 ECTS in the areas of quantum mechanic, statistical physics, computational and mathematical methods