Based in Quito, Ecuador 🌎
Bridging the gap between advanced academic research and scalable enterprise AI.
I am a professional Data Scientist and AI Architect with 6 years of experience in the data science ecosystem, including 2 years of specialized experience in Data Engineering. I focus on building intelligent, robust, and scalable systems using cutting-edge models.
- 🔭 Currently Working On: Applied research for an autonomous investment engine powered by LLMs (for real-time market sentiment on assets like NVDA, TXN, and DIS), and developing a patentable patient-classification process where LLMs function as a discrete reasoning step.
- 🌱 Currently Exploring: Advanced Agentic AI workflows, Knowledge Graphs (GraphRAG), and optimizing responses using models like Gemini, Llama, and Claude.
- 🏗️ Architecture Philosophy: Strong advocate for Hexagonal Architecture (Ports and Adapters) to maintain strict separation between business logic and infrastructure in data projects.
- 📚 Continuous Learning: Recently completed the McKinsey Forward Program focusing on business leadership, earned the AI Skills Fest 2026 badge, and actively preparing for the IELTS exam.
| Category | Technologies |
|---|---|
| Core Languages | Python 3 |
| Data Engineering | Pandas, NumPy, Medallion Architecture Pipelines |
| AI & LLMs | LangGraph, Prompt Engineering, Gemini, Llama, Claude, Riverml |
| Architecture | Hexagonal Architecture, Microservices |
| Databases | Neo4j (Graph), Vector Stores |
| DevOps & Tools | Docker, Git, GitHub Actions |
- 🪐 Exoplanet GraphRAG: A production-oriented Knowledge Graph platform utilizing Neo4j and LangGraph to ingest and analyze NASA exoplanet datasets.
- 📈 Asuskf Invest: An autonomous investment engine and frontend dashboard tracking the semiconductor and healthcare sectors using deterministic logic and LLM sentiment analysis.
- 🏥 HealthTech Data Pipelines: Engineered robust extraction and organization pipelines for processing complex medical reports in Spain.
"Optimizing systems, extracting knowledge, and driving high-performance execution."