Systems Engineering student · Software Development & Applied AI
Exploring the intersection of machine learning, industrial technology, data engineering, and full-stack software — with a background in mechatronics.
- Applied AI & computer vision: image classification, model evaluation, and inspection workflows.
- Industrial systems & IoT: sensor simulation, condition monitoring, and predictive maintenance experiments.
- Data science & engineering: ETL pipelines, SQL, data quality, and business analytics.
- Software engineering: accessible web interfaces, REST APIs, databases, and reproducible local development.
| Project | Focus | What it demonstrates |
|---|---|---|
| VisionQC AI | Computer Vision · OpenCV · scikit-learn | Image-based industrial surface inspection, defect classification, model evaluation, and human review. |
| ForgePulse | Industrial IoT · Machine Learning | Simulated sensor telemetry, equipment monitoring, predictive risk scores, and alerts. |
| Zentry | React · TypeScript · Supabase | Personal finance tracking with accounts, transactions, budgets, and analytical views. |
| DataBridge | Python · ETL · SQL | Multi-source data ingestion, validation, duplicate handling, and traceable pipelines. |
| MercadoLens | Python · Pandas · Business Intelligence | Sales and inventory analytics with a SQL star schema and interactive dashboards. |
| DocuMind | NLP · Information Retrieval · FastAPI | Document search with source-linked answers, relevance ranking, and evidence checks. |
VisionQC AI · Computer vision & industrial inspection |
ForgePulse · Industrial monitoring & predictive maintenance |
DataBridge · Data engineering |
MercadoLens · Data analytics |
| Area | Tools & technologies |
|---|---|
| Programming | Python, TypeScript, Java, SQL, JavaScript |
| Applications & APIs | React, FastAPI, REST APIs, HTML/CSS |
| Data & machine learning | Pandas, NumPy, scikit-learn, OpenCV, SQLite, PostgreSQL |
| Platforms & workflow | Git, GitHub Actions, Supabase, Docker (learning) |
I'm strengthening my understanding of model evaluation on real-world datasets, scalable data pipelines, industrial software, and production-ready development practices. I value clear documentation, reproducible examples, and being able to explain the engineering decisions behind each project.
Interested in software engineering, AI, data, and industrial technology opportunities.
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Some featured projects use synthetic data and simulated industrial processes. Their individual repositories describe the scope, methodology, and limitations.