Backend & APIs
PythonFastAPINode.jsExpress.jsREST APIsJWT
Software engineering
Backend systems, distributed communication, and software that can be understood.
My software engineering work spans customer-facing backend applications, API-driven services, distributed messaging, observability, asynchronous processing, and AI-enabled software. I care about explicit interfaces, failure handling, testing, and understanding what happens underneath the framework.
Engineering capabilities
Distributed systems
RabbitMQgRPCService DiscoveryAsyncIOOpenTelemetry
Data
PostgreSQLMongoDBSQLiteWeaviate
Infrastructure
DockerGitLab CI/CDLinuxAWS LambdaKubernetes exposure
Selected engineering work
Projects that expose the engineering decisions.
Backend + Applied AI
DeepFeed AI
A personalized knowledge-discovery platform with FastAPI application services, explainable recommendation scoring, feedback-driven user modeling, event-driven processing, agentic adaptation, observability, and CI.
PythonFastAPIPostgreSQLRabbitMQOpenTelemetry
View case study →Real-Time Systems · System Design
Real-Time Collaborative Document Platform
An architecture-oriented collaborative editing platform exploring WebSocket event flows, persistent document state, service boundaries, and the trade-offs between operations, snapshots, and concurrent editing.
WebSocketsPostgreSQLDistributed SystemsSystem Design
Conversational AI · Stateful Backend
Agentic Chatbot
A FastAPI conversational backend with JWT-scoped users, persistent three-layer memory, conflict resolution, SSE streaming, LLM self-evaluation, PostgreSQL, and distributed tracing.
PythonFastAPIPostgreSQLSSEOpenTelemetry
View case study →Professional foundation
Customer-facing backend engineering
At Treeloq (formerly Priwoo), I built backend and full-stack application features with Node.js, Express.js, MongoDB, React, REST APIs, JWT authentication, third-party integrations, AWS Lambda, and Puppeteer-based PDF workflows.
R&D foundation
Distributed and multi-agent software
At CodeGen International, I worked across Java and Python agent systems, RabbitMQ, gRPC, Weaviate, Docker, GitLab CI/CD, and distributed tracing. I implemented end-to-end OpenTelemetry tracing across a 10–12+ agent orchestration framework and visualized execution through Jaeger and Grafana.
Engineering approach
I prefer software where data flow, ownership, interfaces, and failure behaviour are explicit. Frameworks are useful, but I want to understand the system well enough to validate generated code, debug failures, and explain the trade-offs behind the implementation.