Backend and AI Systems Engineer with a proven track record of designing high-level system topologies and building reliable, scalable backend architectures, alongside designing agentic AI systems, tool-calling pipelines, and RAG architectures.
Deeply focused on automated testing, clean system design, scalable event-driven architectures, and production-ready agentic AI systems.
I am a Backend and AI Systems Engineer with a proven track record of designing high-level system topologies and shipping core enterprise modules to production. My work balances two foundational disciplines: scalable, event-driven backend engineering and autonomous, tool-calling AI agent systems.
Deeply focused on automated testing, clean object-oriented domain modeling, and building reliable, asynchronous microservices with Spring Boot, Oracle, PostgreSQL, Redis Streams, and RabbitMQ in production environments.
In AI engineering, I architect deterministic agentic workflows—including multi-agent verification pipelines, native tool calling, closed-loop test synthesis engines with compiler error reflection, and high-throughput semantic RAG ingestion workers under strict token budgets.
Designing verification pipelines that correlate project issue criteria with code diffs using deterministic structured outputs and native tool-calling, executing tests in sandboxed containers and reflecting on compiler logs to self-heal code.
Handling high-throughput unstructured streaming data with distributed queues (Redis Streams, RabbitMQ), non-blocking connection pools, deduplication, and semantic vector indexing with strict token-budget constraints.
Deep commitment to automated testing with JUnit, Mockito, and EvoSuite to minimize regressions, combined with formal verification principles to ensure deterministic timing constraints in complex systems.
A comprehensive matrix of Java JVM capabilities, AI agent orchestration patterns, and distributed system tools.
Deep knowledge of JVM concurrency, memory modeling, and clean system design.
Enterprise backend architectures, RESTful APIs, Spring Data, and security.
Complex relational domain entities, ORM mapping, and query performance optimization.
Asynchronous workers (asyncio, asyncpg), data streaming, and vector integration.
Autonomous closed-loop pipelines, native tool calling, and multi-agent coordination.
Integrating LLMs into enterprise Java, deterministic structured JSON outputs, and prompt chaining.
Semantic vector indexing, real-time context retrieval, deduplication, and token budget management.
Recursive reflection on compiler and assertion failure logs in sandboxed Docker containers.
Relational schema design, schema optimization, and non-blocking connection pools.
Asynchronous event-driven architecture, distributed stream queues, backpressure handling.
Comprehensive unit and integration test suites maintaining high code coverage and minimizing regressions.
Sandboxed container execution, CI/CD pipelines, engineering workflow alignment, and API documentation.
Directly connected to the GitHub REST API with live commit telemetry, real-time stars, and active repositories.
Deep dives into modern backend architectures, event-driven streaming pipelines, and autonomous AI agent system design.
When synthesizing automated test suites from requirements and code diffs, LLMs can produce syntax or assertion mismatches. Here is how we design a closed-loop multi-agent reflection engine that runs in Docker sandboxes and self-heals broken code autonomously.
Ingesting continuous streams of unstructured group communications and messages requires robust backpressure handling, non-blocking storage, and tight context-window budget management.
In real-time and autonomous systems, data freshness between sensor inputs and output actuators must be formally guaranteed. Here is how custom Java graph traversal algorithms automate this verification.
Eliminating regressions in complex enterprise backends requires more than happy-path tests. How we combine JUnit 5 parameterized testing, Mockito boundary stubs, and EvoSuite automated test generation to achieve resilient code quality in production environments.
Scaling asynchronous enterprise communications requires clear event topologies, dead-letter queuing, and transactional outbox patterns. Here is how we balance Redis Streams and RabbitMQ for deterministic event delivery.
Direct access to published articles, engineering updates, skills, and verified recommendations on LinkedIn.
Java & AI Systems Engineer | Agentic Workflows, Spring Boot & Distributed Systems