Work
A selection of machine learning, AI, data engineering, MLOps, and software engineering projects developed through professional work, independent development, and academic study.
End-to-end machine learning system for AAPL stock-price forecasting, covering chronological data splitting, leakage-safe preprocessing, stacked LSTM modeling, hyperparameter optimization, experiment tracking, model management, evaluation, API-based inference, containerization, automated testing, CI/CD, and cloud deployment.
Production-oriented market data pipeline that acquires financial data from yfinance, validates and transforms the data, stores it in PostgreSQL using idempotent loading, and provides an API layer for querying the stored data. Apache Airflow is being integrated for pipeline scheduling and orchestration.
Document intelligence pipeline for processing PDF and DOCX documents, including text extraction, document preprocessing, OCR for scanned documents, text processing, embeddings, and vector-database foundations for semantic search and document retrieval.
End-to-end ETL pipeline for processing retail order data from CSV files and loading validated results into PostgreSQL. The project includes separate extraction, transformation, and loading stages, data-quality checks, incremental and idempotent loading, structured logging, automated testing, credential management, and containerized execution.
Machine learning inference service built around a REST API for serving model predictions, with request and response validation, environment-based configuration, structured logging, automated testing, modular model handling, and Docker-based deployment.
To explore more of my projects and work, visit the links below.