🌐 Language: 🇬🇧 English | 🇪🇸 Español

I build reliable software systems that combine backend engineering, artificial intelligence, automation, and modern web technologies.

My work ranges from AI and machine-learning experiments to real-world backend services, business automation, APIs, integrations, and full-stack applications.

I care especially about software architecture, maintainability, testing, security, and building systems that solve practical problems.

Python · Rust · PHP · C# · SQL · PostgreSQL · FastAPI · Flask · Axum · Laravel

TypeScript · JavaScript · Angular · React · Next.js

Machine Learning · Reinforcement Learning · LLMs · AI Agents · RAG · Chatbots · Process Automation

Docker · Git · GitHub Actions · Linux · CI/CD · REST APIs · WebSockets

A multi-agent reinforcement-learning environment built around a real-time Pong game.

• Tabular Q-learning agents with competitive self-play
• PhaserJS interactive game environment
• Flask-SocketIO backend
• Real-time communication through WebSockets
• Training, evaluation, reward tracking, and persistent Q-tables
• Backend and frontend automated tests
• Docker-based development environment

Python Flask PhaserJS WebSockets Reinforcement Learning Docker

A configurable Python automation service for monitoring currency exchange rates and triggering notifications.

• External exchange-rate API integration
• Threshold-based email alerts
• SMTP integration
• Environment-based configuration
• Structured logging
• Modular object-oriented architecture
• Automated scheduled execution

Python REST API SMTP Automation

AI / ML Engineering
├── LLM applications & AI agents
├── Reinforcement learning
├── Retrieval-Augmented Generation
└── ML-powered backend systems

Software Engineering
├── Backend architecture
├── Distributed & real-time systems
├── Security and traceability
├── Testing & software quality
└── Domain-driven application design

Automation
├── Business process automation
├── API integrations
├── Chatbots
└── Intelligent workflows

I aim to build software that is:

• Maintainable — clear boundaries, readable code, and intentional abstractions
• Reliable — testing, validation, observability, and defensive engineering
• Secure — least privilege, secure defaults, and careful data handling
• Traceable — changes and important operations should be auditable
• Simple — complexity should exist only when the problem requires it

GitHub Foundations

Building software at the intersection of engineering, artificial intelligence, and automation.