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.
• 🎸 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

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. 💾🎸🌻