Data systems
Reliable ingestion, transformation, storage, and observability for useful data.
I'm a Master's student in Big Data and AI with a background in computer science and web development.
I build data pipelines, machine-learning systems, and focused web interfaces that make complex results useful.
Data Engineering Intern · Machine Learning Engineering Intern · Applied AI Intern
End-to-end systems, measured results, and usable interfaces.
The intersection of data engineering, applied machine learning, and product-minded web development.
Reliable ingestion, transformation, storage, and observability for useful data.
Models evaluated against baselines and delivered through reproducible workflows.
Retrieval, document intelligence, and AI features designed around evidence and limits.
Clear interfaces that help people understand and act on technical results.
A small set of end-to-end projects. Each one should explain the problem, system, evaluation, and tradeoffs.
A replayable streaming pipeline that detects unusual behavior in high-volume event data and explains each alert.
A retrieval system for trustworthy answers over real documents, with citations, evaluation, and explicit failure handling.
A reproducible forecasting workflow that compares models against honest baselines and exposes uncertainty through a decision interface.
Technical notes about experiments, systems, and lessons learned.
Technical writing will appear here as you document experiments, tradeoffs, and results.
The technologies I am using to build and explain the work in this portfolio.
Python · SQL · TypeScript · Bash
Apache Spark · Kafka · dbt · Airflow
scikit-learn · PyTorch · MLflow · Model evaluation
PostgreSQL · DuckDB · ClickHouse · MinIO / S3
FastAPI · Astro · Docker · GitHub Actions
In progress — add your specialization, thesis, or relevant coursework.
Add the strongest coursework, project, or academic result.
Add the technologies and practical work that started your path.