Project engineer with experience in Oil&Gas and Shipping Ops, with a master in Data, Data Engineering and AI.
I bring experience in project coordination and operational analysis, complemented by academic and personal projects in Python, machine learning, Power BI and cloud deployment. My portfolio includes learning projects and prototypes using Azure, Databricks and MLflow.
"From heavy industry to artificial intelligence: I transform data into decisions."
Tools explored through coursework and projects; the repository descriptions explain their scope.
| Project | Description | Stack |
|---|---|---|
| foundry-learn-chat | Microsoft Learn exercise after AI-901: a small chat app with conversation history and real Azure execution checks | Microsoft Foundry · Python · Responses API · Entra ID |
| azure-ml-mlflow-lab | Microsoft Learn adaptation for comparing regressors, tracking experiments and running Azure ML command jobs | Azure Machine Learning · MLflow · scikit-learn |
| graph-rag-playground | Interactive movie-graph demo with grounded answers and execution traces · Try the app | LangGraph · Neo4j · Groq · MLflow · Streamlit |
| churn-mlops-databricks | Learning project covering data preparation, model training, MLflow tracking and Databricks serving | Python · PySpark · Databricks · Delta Lake · MLflow |
| azure-price-scraper | HTTP-triggered Azure Function for extracting product names and prices from compatible HTML | Python · Azure Functions · requests |
| powerbi-dashboards | Business intelligence dashboards for KPI monitoring with Power BI | Power BI · DAX · SQL |
- Microsoft Certified: Azure AI Fundamentals (AI-901) — Microsoft, October 2026 · Exam score: 900/1000
- Master in Big Data, Data Engineering and AI — ESESA Business School (2025-2026)
- Financial Analysis and Modeling Professional Certificate — Corporate Finance Institute (CFI), LinkedIn Learning
- MLOps Essentials: Monitoring Model Drift and Bias — LinkedIn Learning
- Complete Guide to Python Fundamentals for MLOps — LinkedIn Learning
- 42 Málaga — Fundación Telefónica (C, Linux, DevOps)
- Python and machine learning — Strengthening fundamentals and model evaluation
- MLOps — Experiment tracking, serving and reproducible workflows
- Microsoft Foundry and Azure Machine Learning — Applying Microsoft Learn exercises to chat applications and MLflow experiment tracking
- RAG — Retrieval, generation and evaluation in small prototypes
⭐ Open to opportunities in Data roles — Product / MLOps — Málaga, Spain