Software Engineer focused on building scalable backend systems, distributed architectures and cloud-native solutions.

I work with high-criticality systems, microservices, APIs, messaging and architectural evolution, with a growing focus on applying AI to software engineering and development workflows.

Currently exploring AI-Driven Engineering, Spec-Driven Development, RAG systems and modern approaches to building reliable software.

Microservices · Event-Driven Architecture · Kafka · Messaging · APIs · Resilience · Eventual Consistency

Kubernetes · Docker · Cloud-Native · CI/CD · Observability · Scalability · Infrastructure as Code

Kotlin · Java · Quarkus · .NET · PostgreSQL · Redis · REST · Distributed APIs

RAG · LLM Applications · AI-Assisted Development · Spec-Driven Development · Automated Testing · Engineering Workflows

Personal training management application focused on modern mobile architecture, offline-first capabilities and Spec-Driven Development.

React Native · Expo · TypeScript · SQLite · Zustand

Offline-first Android application built with Kotlin and Jetpack Compose.

Kotlin · Jetpack Compose · Android · Offline-first

🚧 Still in development.

Planned AI Engineering project focused on building and evaluating a production-oriented RAG system with source traceability, retrieval evaluation and LLM-based answers.

Kotlin · Quarkus · PostgreSQL · pgVector · RAG · LLM

🚧 Planned / in development.

Planned reference implementation of resilient event-driven architecture using Kafka, transactional outbox, idempotent consumers and distributed observability.

Kotlin · Quarkus · Kafka · PostgreSQL · Redis

🚧 Planned / in development.

Planned Cloud-Native platform focused on Kubernetes, Infrastructure as Code, CI/CD, observability and scalable deployments.

Kubernetes · Docker · Terraform · Helm · GitHub Actions

🚧 Planned / in development.

Collection of software design patterns covering Creational, Structural and Behavioral patterns.

C# · Design Patterns · Software Engineering

Some of my most relevant experiments and products are private while they are being developed.

Private Engineering Project

AI-assisted engineering governance layer for Spec-Driven Development, automated quality gates and controlled software delivery.

The project remains private and is not intended to be presented as an open-source repository.

Gamified educational platform focused on microlearning, active practice and personalized learning experiences.

Next.js · Kotlin · Quarkus · PostgreSQL · Redis · PWA

SaaS platform for technical pre-interview assessment of restaurant candidates, focused on structured evaluations, automated correction and secure candidate flows.

Next.js · TypeScript · Supabase · PostgreSQL · PWA

My current direction combines four areas:

                    Software Engineering
                            │
          ┌─────────────────┼─────────────────┐
          ▼                 ▼                 ▼
       Backend        Distributed Systems    Cloud
          │                 │                 │
          └─────────────────┼─────────────────┘
                            │
                            ▼
                    AI Engineering
                            │
                            ▼
                 Software Architecture

The goal is not only to build applications, but to explore how architecture, automation and AI can improve the way software is designed, developed, tested and operated.

I'm particularly interested in:

• Designing resilient distributed systems
• Event-driven architectures
• Cloud-native backend platforms
• Architecture and scalability
• AI-assisted software engineering
• RAG and LLM-based systems
• Automated testing and quality gates
• Spec-Driven Development
• Engineering productivity and developer workflows

I use personal projects to experiment with architecture, technologies and engineering practices that can be applied to real-world software systems.

Some repositories are production-oriented experiments, some are architectural references, and others are ongoing explorations.

The goal is to keep learning by building, measuring, documenting and improving.

_{Building software, exploring architecture, and applying AI to engineering.}