Javascript Developer & Node.js Developer & React Developer

Kandidát je fullstack vývojár/inžinier, ktorý pracuje najmä s Node.js, s ktorým má viac ako rok komerčných skúseností, ale počas školy s ním pracoval na čiastočný úväzok štyri roky. Na svojom poslednom projekte vyvíjal softvér na správu taxíkov. Každé auto malo zariadenie GPS, ktoré odosielalo informácie o polohe, najazdených kilometroch atď. a dalo sa sledovať v priamom prenose. Z technológií tam používal Node.js, React, Typescript, MongoDB, PostgreSQL. Okrem Node.js sa vyzná aj v strojovom učení, dátovej vede, vie pracovať s neurónovými sieťami, pozná dátovú analytiku, Javascript, Docker, Python, Flask, Jupyter. V budúcnosti by sa chcel zamerať na vývoj backendu. Angličtinu ovláda na úrovni B2 a očakáva, že na projektoch bude komunikovať v angličtine. Má skúsenosti s agilnými metodikami a nemá problém s ich používaním.

Full Stack Software Engineer (05/2017 - 06/2022): Developed web applications in the transport and taxi domain, along with side projects based on customer requirements. Utilized technologies such as Node.js, React, TypeScript, MongoDB, PostgreSQL, Redis, Jupyter, Kubernetes, and Docker. Worked on a team of 5 developers.

Full Stack Developer (09/2019 - 05/2020): Developed an AI chart recommender system in data analytics and presentation online web tool. Implemented a machine learning model accessible via REST API. Collaborated with Instarea on a year-long school project. Used Python, Docker, ML, Flask, and Jupyter. Worked on a team of 5 developers.

Engineer (2019 - 06/2021): Completed a thesis on deep neural networks for information retrieval from medical image data. Specialized in segmentation using deep learning methods in the domain of medical image data, specifically organs at risk in CT scans. Proposed and evaluated a multi-step segmentation method using two neural networks. Worked on a team of 3 developers.

Bachelor (2016 - 2019): Conducted research on personalized recommendation systems in the domain of interesting places using the Yelp public dataset. Proposed a method based on collaborative filtering with ML clustering by business features to reduce rating sparsity. Worked on a team of 3 developers.

High School Diploma (2008 - 2016): Graduated from Gymnázium Antona Bernoláka v Námestove.

Language Skills: Slovak (Native), Other Language Skills: English (C1).

Digital Skills: Restful API, JavaScript ES6/TypeScript, Web Development, Linux, Git, Node.js, React.js, Docker & Kubernetes, NoSQL (MongoDB), Microsoft Office, CSS, Recommender Systems, Bootstrap, Data Science, Deep Learning, JSON, Software Engineering, Python, SQL, Machine Learning, HTML, Programming, JavaScript.