Backend developer focused on data-intensive backend systems, event-driven architecture, and retrieval infrastructure.

I build backend systems around explicit state, durable data ownership, asynchronous integration, and observable behavior — from recommendation pipelines and graph-augmented retrieval engines to IoT/payment systems where backend state controls real-world access.

My work sits at the intersection of backend engineering, data modeling, and applied ML/AI. I am especially interested in systems where correctness under partial failure, derived data, retrieval quality, and long-term evolvability matter.

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Completing WEG's CentroWEG / SENAI Industrial Apprenticeship Program in Systems Development, with current backend work centered on service-oriented architecture, distributed-system reliability, and graph-augmented retrieval.

• Leading backend architecture for Portal Conecta, focused on service boundaries, OpenAPI contracts, RabbitMQ messaging, and explicit synchronous/asynchronous integration.
• Evolving VellumHub v4 with transactional outbox, idempotent consumers, Flyway migrations, correlation ID propagation, observability, and Testcontainers-based distributed-flow tests.
• Refining Kairos v1 toward HippoRAG-style retrieval: passage-aware graph propagation, triple recall, recognition filtering, per-user graph isolation, and retrieval trace persistence.
• Studying distributed systems and derived data through Designing Data-Intensive Applications and applied Java/Spring work.

• Distributed backend systems
• Event-driven architecture
• Data modeling and derived read models
• Graph-augmented retrieval
• Vector search and recommendation systems
• Reliability under partial failure
• Backend quality: tests, boundaries, migrations, observability, and failure-safe flows

Recommendations without synchronous coupling.

Event-driven book recommendation platform implemented as five JVM services: gateway, user, catalog, engagement, and recommendation.

The core architectural decision is that recommendation-service does not call source-of-truth services during the recommendation hot path. Instead, catalog, user, and engagement changes are propagated through Kafka and materialized into recommendation-owned read models: book embeddings, user profile vectors, and pre-joined metadata.

This keeps personalized recommendation serving local, fast, and resilient under partial failure.

Key engineering decisions

• Read model isolation through Event-Carried State Transfer.
• Recommendation-owned derived state instead of query-time service coupling.
• pgvector similarity search over 384-dimensional embeddings.
• Incremental user-profile learning from rating events classified as DETRACTOR, NEUTRAL, or PROMOTER.
• Cold-start profile seeding from onboarding genre preferences.
• Retry-safe asynchronous processing with idempotent consumers, retry topics, and dead-letter topics.
• v4 work around transactional outbox, schema migrations, correlation IDs, observability, and distributed-flow testing.

Java 21 · Spring Boot · Spring WebFlux · Kafka · PostgreSQL · pgvector · Redis · LangChain4j · Flyway · Docker · Testcontainers

Documents as a self-building semantic memory graph.

JVM-native graph-augmented retrieval backend that turns documents into a semantic memory graph.

Kairos combines dense vector search with graph traversal. PostgreSQL/pgvector stores embeddings for dense recall; Neo4j stores passages, concepts, triples, synonymy relations, and semantic graph structure. The system is designed as retrieval infrastructure, not as a chatbot wrapper.

Key engineering decisions

• Dual-store retrieval architecture: pgvector for dense search, Neo4j for structural reasoning and graph propagation.
• Local embedding generation with ONNX Runtime using all-MiniLM-L6-v2 384-dimensional embeddings.
• Gemini/Spring AI integration for structured subject-predicate-object triple extraction.
• Personalized PageRank over graph anchors for concept propagation.
• Dense passage recall, triple recall, recognition filtering, and Reciprocal Rank Fusion.
• Ingestion-time synonymy edges, so lexical variation becomes graph structure instead of query-time fuzzy matching.
• Per-user graph isolation treated as a first-class data-modeling concern.
• Retrieval trace persistence for observability and future ranking improvements.

Java 21 · Spring Boot · Spring AI · ONNX Runtime · PostgreSQL · pgvector · Neo4j · Neo4j GDS · Gemini · Docker

Physical access controlled by durable backend state.

Reactive IoT parking access-control system where backend-confirmed payment state controls physical access.

OpenIT integrates ESP32 sensors, MQTT, Node-RED orchestration, a Spring WebFlux backend, Mercado Pago Checkout Pro, MySQL persistence, and a React/TypeScript payment terminal. The project covers the full flow from physical sensor events to payment confirmation and gate release.

Key engineering decisions

• Gate release is tied to persisted backend payment confirmation, not optimistic UI state.
• ESP32 sensor events are propagated through MQTT and Node-RED.
• Webhook-based payment confirmation is persisted in MySQL.
• Server-Sent Events update the frontend payment status without polling.
• Hardware orchestration is separated from backend business rules.
• Payment, access control, persistence, and real-time update concerns are isolated.

Java 21 · Spring Boot · Spring WebFlux · MySQL · MQTT · ESP32 · Node-RED · Mercado Pago · React · TypeScript · Docker

Social relationships modeled as a graph.

Graph-based social network backend built around Neo4j relationships and modular backend architecture.

It models people, connection requests, accepted bidirectional relationships, posts, and network visualization endpoints.

Key engineering decisions

• Neo4j is used for relationship-first social graph modeling.
• Connection requests have an explicit lifecycle with accepted/rejected transitions.
• Authentication and authorization are handled through JWT and role-based access control.
• Backend modules are separated across auth, person, connection, request, post, and graph concerns.

Java 21 · Spring Boot · Neo4j · Spring Security · JWT · Docker · Testcontainers

Languages
Java · SQL · TypeScript · Python · JavaScript · C

Backend
Spring Boot · Spring WebFlux · Spring Security · Spring AI · REST APIs · JWT · SSE · JPA/Hibernate · JDBC · OpenAPI/Swagger

Distributed Systems & Messaging
Kafka · RabbitMQ · MQTT · Event-Carried State Transfer · Transactional Outbox Pattern · Idempotent Consumers · Retry Topics · Dead Letter Topics · Correlation ID Propagation

Data & Storage
PostgreSQL · pgvector · Neo4j · Neo4j GDS · Redis · MySQL · write/read model separation · derived data · schema evolution

AI & Retrieval
LangChain4j · ONNX Runtime · Gemini · RAG · Graph-Augmented Retrieval · Vector Search · Personalized PageRank · Reciprocal Rank Fusion · Embeddings

Architecture
Hexagonal Architecture · DDD · Bounded Contexts · Clean Architecture · Eventual Consistency · CQRS

Infrastructure & Tooling
Docker · Flyway · GitHub Actions · Maven · Git · Linux · Testcontainers

Testing
JUnit 5 · Mockito · Testcontainers · JaCoCo

• Confluent Certified Data Streaming Engineer — Foundations
• Confluent Apache Kafka Fundamentals Accreditation
• Neo4j Graph Data Science Certification
• Neo4j & Generative AI Certification
• Neo4j Fundamentals
• AWS Academy Graduate — Cloud Foundations
• AWS Academy Graduate — Generative AI Foundations

Relevant coursework at WEG CentroWEG / SENAI: API Programming, Database Implementation, System Architecture, Cloud Computing, and Information Security.

Backend or data-intensive systems engineering roles — ideally in teams working on distributed systems, retrieval infrastructure, data pipelines, or applied ML/AI backends.

I am interested in environments where correctness under partial failure is taken seriously, data modeling is a first-class concern, and backend systems are designed to evolve over time.

Open to junior positions and internships where I can contribute to production backend code from day one.

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