Roberto Villafuerte
AI Engineer · MLOps · RAG Systems
Summary
Final-year Computer Science undergraduate specializing in MLOps and retrieval-augmented generation (RAG) systems. Experience architecting automated forecasting pipelines and deploying multi-tenant GenAI support agents. Completed Master's-level coursework in Trustworthy ML at the University of Helsinki. Focused on shipping scalable, reliable, and bias-aware production AI systems.
Experience
AI Engineer
COMPUMAX · Part-time, Remote
- Architecting a multi-tenant RAG helpdesk for small businesses
- Designing end-to-end backend in Python, from prototype to deployment
- Building internal GenAI workflows aligned to business goals and scalable architecture
Machine Learning Engineer Intern
Datalysis Group · Remote
- Automated demand forecasting pipeline for inventory replenishment
- Implemented end-to-end workflow: cleaning/validation, feature engineering, training, evaluation
- Stack: Snowflake, Mage AI, Python
Education
B.Eng. Computer Science
Universidad San Francisco de Quito (USFQ)
Focus: software engineering, algorithms, databases, applied machine learning. Expected graduation: Dec 2026.
Exchange Studies — Data Science (Master's-level)
University of Helsinki
30 ECTS · GPA: 4.83/5.0
Coursework: Big Data Platforms, Computer Vision, Engineering of ML Systems, Trustworthy ML, Software Architecture.
Certifications
Technical AI Safety Course
BlueDot Impact
General Data Protection Regulation (GDPR)
Packt
IBM Machine Learning Specialization
Coursera
Awards
2nd Place — IEEEXtreme 18.0 (Ecuador)
Global 24-hour algorithmic programming competition organized by IEEE