Java & microservices
Designing cohesive services around business capabilities, explicit API contracts and transaction boundaries. Maintainable Java and Spring code supported by clear responsibilities and meaningful tests.
Java & Microservices
Distributed Systems · AI Integration
I build Java-based backend services and microservice architectures for enterprise applications. My work spans asynchronous messaging, data processing and service integration. I connect this foundation with AI agents and RAG, with a focus on clear service boundaries, controlled tool access and observable workflows.
From Java services to container orchestration and AI workflows: I approach architecture through service ownership, data consistency, failure handling and operational visibility.
Designing cohesive services around business capabilities, explicit API contracts and transaction boundaries. Maintainable Java and Spring code supported by clear responsibilities and meaningful tests.
Choosing synchronous or event-driven communication to match the workflow. Considering delivery guarantees, idempotency, retries and consistency across Kafka, RabbitMQ and Redis integrations.
Working with containerized services and Kubernetes deployment patterns. Focusing on reproducible environments, configuration, health probes, resource limits and safe rollout behaviour.
Making systems easier to release and diagnose through CI/CD, structured logs and useful metrics. Connecting application behaviour to operational signals and actionable troubleshooting.
Choosing data access and transaction strategies around the workload. Thinking through query costs, caching, concurrency and consistency rather than treating storage as an isolated component.
Extending backend workflows with LLM integration, retrieval and tool calling. Keeping model output behind explicit contracts, with scoped permissions, source context and evaluation criteria.
Personal engineering projects and AI system design, informed by enterprise software experience.
A personal engineering project exploring banking workflows through microservices, event-driven communication and observability.
AI workflows that retrieve knowledge, call tools and produce evaluable outputs. Focused on RAG, vector search and controlled automation.
Enterprise engineering experience across application ecosystems, banking, university operations and healthcare.
I develop backend microservices with Java and Spring Boot as part of a platform processing over 100 million application records. My work includes asynchronous data flows with Kafka, caching with Redis and data operations with MySQL. I contribute to API integration for Vue.js modules and service operations on Huawei Cloud.
I developed enterprise web applications with Java, Spring, Hibernate and PrimeFaces. Alongside Oracle queries, business logic and application maintenance, I worked on WebLogic/JBoss deployments and Jenkins-supported delivery workflows.
I developed backend components for banking applications with Java, Spring and Hibernate. I worked on service flows and integrations within an ESB architecture using IBM Integration Toolkit, alongside Oracle PL/SQL, PrimeFaces and Bamboo.
I used Java, JavaScript and SQL in patient registration, appointment management and clinical data processing modules. I contributed to application development, troubleshooting and maintenance supporting hospital operations.
Computer Engineering
Computer Engineering
Tools from my professional experience and ongoing engineering practice, alongside AI-focused areas of exploration.
My engineering approach starts with business constraints, service boundaries and operational requirements. Working across application platforms, banking, education and healthcare has shaped how I approach data consistency, integration and production troubleshooting.
I evaluate architecture through its trade-offs: synchronous versus asynchronous communication, caching versus consistency, and abstraction versus operational complexity. With Java and Spring at the core, I value explicit contracts, maintainable code and delivery practices that make services easier to operate.
I apply the same discipline to AI agents and RAG: retrieval quality, controlled tool access, failure handling and measurable evaluation. My focus is connecting intelligent capabilities to dependable backend workflows with clear responsibilities.