Vagif Aliyev
Co-Founder at Stealth Startup
Enschede, Overijssel, Netherlands
Summary
👤
Senior🎓
Top SchoolVagif Aliyev is a prolific young software engineer and repeat founder with a decade of hands-on experience building AI-driven products and startups from Europe. He founded and exited Snapstudy—an AI EdTech app that won multiple awards including a $50K Google Cloud prize and reached 1,400+ DAU—and later served as founding engineer at Upword AI, where his optimizations cut costs and improved NLP backend support. Now co-founder of a stealth startup, Vagif combines practical ML engineering (content-based recommender work on the Palashio/libra repo) with end-to-end product delivery across backend, CV, and automation tooling. He blends industrial design training from the University of Twente and Stanford CS study with an unusual early-career depth: impactful R&D internships and production deployments while still a teenager. Known for squeezing operational wins out of ML systems (>$6k annual savings) and shipping customer-facing AI features, he thrives at the intersection of research, product, and scalable engineering.
10 years of coding experience
4 years of employment as a software developer
Bachelor of Science - BS Industrial Design Engineering, Bachelor of Science - BS Industrial Design Engineering at University of TwenteComputer Science, Computer Science at Stanford Summer Session
German, French, English, Azerbaijani
Stackoverflow
234reputation
40kreached
6answers
12questions
Github Skills (16)
cosine-similarity10
scikit-learn10
pandas10
machine-learning10
python10
scikit10
neural-network8
artificial-neural-networks8
cx-freeze6
selenium-webdriver6
google-chrome-extension6
google-chrome6
selenium6
excel6
pygame6
Programming languages (4)
Github contributions (5)
Palashio/libra
Jul 2020 - Jul 2020
Ergonomic machine learning for everyone.
Role in this project:
ML Engineer
ML EngineerContributions:19 commits, 7 PRs, 1 comment in 6 days
Contributions summary:Vagif's commits primarily involve the development of a content-based recommender system. This includes implementing classes and methods for data preprocessing, feature extraction, and calculating cosine similarity. The user modified existing code and added new functionalities for recommending items based on content. This indicates a focus on building and integrating machine-learning components into the project.
pythonmachine-learning-for-everyonedata-scienceneural-networksmachine-learning
Vagif12/Ethereum-Fraud-Detection
Oct 2020 - Feb 2021
Contributions:46 commits, 40 pushes, 2 branches in 3 months
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