TKTymofii's Portfolio
Resume

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Tymofii Kalnytskyi

New York, NY | 347-841-4601 | tymofiikalnytskyi@mail.adelphi.edu | AI Engineer / Software Engineer candidate

Summary

Computer Science & Artificial Intelligence student at Adelphi University with a 3.9 GPA, hands-on experience building AI products, static analysis tooling, and polished web interfaces, plus internship experience spanning software instruction, product pitching, and full-stack development.

Education

  • Adelphi University, Garden City, NY
  • B.S. Computer Science & Artificial Intelligence, Graduation Spring 2028
  • GPA: 3.9 / 4.0, Dean's List
  • Coursework: Data Structures & Algorithms, Operating Systems, Database Management, Statistics & Data Analytics, Multivariable Mathematics

Technical Skills

Python, Java, C, C++, JavaScript, TypeScript, SQL, R, HTML5, CSS3, LLMs, NLP, Scikit-learn, Logistic Regression, Ollama, FastAPI, React, Next.js, Node.js, Flask, PostgreSQL, Docker, Git, Linux, Figma, Adobe Creative Suite.

Experience Highlights

  • The Knowledge House, Software Technical Assistant: taught AI, React, JavaScript, HTML, and CSS while mentoring 20+ students.
  • The Knowledge House, Software Developer Intern: completed 100+ hours of project-based training and pitched product prototypes to 25+ funders.
  • ITGenio Coding School, Software Engineering Intern: built full-stack applications and educational games across Python, SQL, JavaScript, C++, and Java.
Resume Highlights

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MemoryChat

Built a full-stack AI chatbot with local LLM inference, stateless multi-turn memory, and a polished custom interface.

AI Bug Finder

Combined AST-based static analysis with Logistic Regression severity scoring inside a modular FastAPI backend.

The Knowledge House

Taught AI, React, JavaScript, HTML, and CSS while mentoring 20+ students through debugging and project work.

ITGenio

Built full-stack applications using Python, SQL, JavaScript, C++, and Java across real project lifecycles.

Machine Learning

Hands-on experience with Scikit-learn, Logistic Regression, NLP-adjacent workflows, and feature-oriented problem framing.

Execution Range

Comfortable moving between product interfaces, API structure, backend logic, and teaching technical concepts clearly.