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I build and deploy end-to-end machine learning systems in production environments on AWS. I specialize in integrating AI into real-world applications from backend APIs to mobile-ready solutions bridging the gap between data science and software engineering.
- End-to-end ML pipelines: data preprocessing, model training, evaluation, and deployment
- Cloud & Production: AWS infrastructure for scalable model serving
- AI in Applications: connecting ML models to apps via FastAPI, Ktor, and Kotlin backends
- Modern AI: comfortable with LLMs and generative AI paradigms
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End-to-end regression pipeline that predicts used car prices based on vehicle features. Covers data cleaning, feature engineering, model training, and evaluation. |
ML classification model for detecting Android malware. Achieved 99.3% accuracy on the primary dataset and 96% accuracy on the secondary dataset. |
Languages and Tools
Kotlin |
CSS |
HTML |
MySQL |
PostgreSQL |
Python |
Anaconda |
Postman |
GitHub |
Git |
Jupyter |
Kaggle |
MongoDB |
NumPy |
OpenCV |
Pandas |
PyCharm |
PyTorch |
TensorFlow |
VSCode |
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Ktor |
FastAPI |
SQLite |
TypeScript |
Node.JS |
Scikit-learn |
SQLite |
Seaborn |
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