I'm Rony, a data and cloud professional working mainly across LATAM business and technology contexts, focused on building reliable data platforms, applied AI systems, and human-centered recommendation engines.

I recently completed my Master’s program with a final GPA of 4.82 / 5.00 — a personal and professional milestone that strengthened my path toward AI, Data Architecture, MLOps, Cloud Engineering, and Machine Learning Engineering.

• 🔭 I’m currently working on projects that combine cloud, data engineering, machine learning, semantic search, and generative AI.
• 🌱 I’m currently deepening my skills in AI Engineering, Data Architecture, MLOps, and Streaming Systems.
• 🚀 Next learning areas: Kafka, Google Cloud Dataflow, Apache Beam, real-time pipelines, and production-grade ML systems.
• ☁️ Ask me about GCP, AWS, PySpark, BigQuery, Cloud Run, Docker, Terraform, CI/CD, and data pipelines.
• 🤖 I’m especially interested in how poetry, music, emotion, and language can inspire semantic recommendation systems.
• 💬 My answers on Stack Overflow in Spanish have reached over 160k readers.
• 📦 I maintain open-source projects such as LuaSF, a lightweight pure-Lua statistics library now revived with tests, documentation, releases, and LuaRocks packaging.
• 🎸 Personal motto: logic meets creativity 💾🎸🌻
• 😄 Pronouns: he / him

Emotional-Semantic NLP and MLOps Project

VersoVector explores poetry and lyrical language as dense forms of emotional, symbolic, and semantic expression. The long-term goal is to evolve it into a mood-aware recommendation engine for poetic and lyrical content.

Main ideas:

• Emotional and thematic multilabel tag prediction.
• Semantic similarity between poems and short texts.
• Topic modeling, clustering, and visual interpretation.
• Modular Python architecture.
• MLOps roadmap: MLflow, model packaging, FastAPI inference, Docker, Cloud Run, Terraform, and CI/CD.
• Long-term vision: explainable emotional-semantic recommendations for poetry, lyrics, and user-provided text.

Repository: VersoVector

RAG-based Real Estate Recommendation System on GCP

A real estate recommendation system for natural-language property search, combining semantic retrieval, structured data enrichment, geospatial visualization, and LLM-based explanations.

Main ideas:

• Retrieval-Augmented Generation for real estate recommendations.
• Semantic search with vector embeddings and FAISS.
• Structured enrichment with BigQuery.
• Explanatory responses using Gemini.
• Frontend and backend deployed on Cloud Run.
• DevOps foundation with Docker, Terraform, GitHub Actions, Artifact Registry, Secret Manager, and Workload Identity Federation.

Repository: MIAD-RAG-RealEstate

Lua Statistics Functions — Pure Lua Open Source Library

LuaSF is a lightweight pure-Lua library for descriptive statistics, random variables, and sampling utilities. The project originally started around 2014 and was recently revived with compatibility fixes, tests, documentation, releases, and LuaRocks packaging.

Main ideas:

• Pure Lua statistics and random variable utilities.
• Backward-compatible API revival.
• Additional helpers for variance, median, quantiles, sampling, weighted choice, and deterministic random generation.
• Tests, examples, changelog, API documentation, and GitHub releases.
• Published as a LuaRocks package.
• Open-source maintenance focused on compatibility, simplicity, and community usability.

Repository: LuaSF
Package: luasf on LuaRocks

I’m currently evolving from data engineering and cloud analytics toward a broader AI engineering profile:

Data Engineering
    ↓
Cloud Data Architecture
    ↓
Machine Learning Engineering
    ↓
MLOps and Model Deployment
    ↓
Streaming and Real-Time Data Systems
    ↓
AI-powered products and recommendation systems

Focus areas:

• AI Engineering and applied machine learning.
• Data architecture for analytical and operational systems.
• MLOps foundations: experiment tracking, packaging, deployment, monitoring, and CI/CD.
• Cloud-native deployments on GCP and AWS.
• Streaming pipelines with Kafka, Apache Beam, and Dataflow.
• Semantic search, embeddings, RAG, and explainable recommendation systems.

• Stack Overflow ES: HubertRonald
• Kaggle: Hubert Ronald
• HackerRank: HubertRonald
• Portfolio: hubertronald.github.io

I believe technology is not only about automation, scalability, and performance.

It is also about understanding patterns: in data, in language, in decisions, and in human experience.

Logic meets creativity. 💾🎸🌻