Backend Engineer with four years of experience specializing in distributed systems, event-driven architectures, and cloud-native platforms. I architect and build reliable, scalable, and observable production systems, with a focus on backend infrastructure for AI-powered applications, resilient architectures, and systems designed to operate reliably under real-world workloads.

My engineering career began in application security as a web penetration tester and vulnerability researcher, where I reported more than 100 security vulnerabilities, ranked among the top researchers on Bugcrowd, and received the MVP Badge (Q3 2020).

One vulnerability report unexpectedly changed the direction of my career. After submitting a report through a public bug bounty program, I was asked to provide a USDT wallet address to receive the bounty. At the time, I had never used cryptocurrency. Curiosity led me into decentralized systems. What started as learning how blockchain payments worked evolved into a deep interest in the infrastructure behind distributed applications.

That journey led to an internship at Polygon, where I contributed to the development of a production-grade multichain NFT marketplace that ranked among the top 3 projects in the program's final hackathon.

Since then, I have focused on designing backend systems that remain reliable at scale and resilient to failure. My work focuses on event-driven architectures, asynchronous processing, real-time communication, distributed messaging, caching strategies, background job systems, and production observability. I have designed centralized logging pipelines, instrumented applications with metrics and distributed tracing, and built monitoring solutions that provide deep visibility into production systems.

My work centers around distributed systems, event-driven architectures, and observability, using technologies such as Kafka, RabbitMQ, Redis Streams, BullMQ, Redis, WebSockets, OpenTelemetry, Prometheus, Loki, Promtail, Grafana, Docker, and cloud platforms including AWS and GCP.

Today, I focus on architecting and building reliable AI systems that can operate effectively in real-world production environments. My work centers on agentic AI architectures, intelligent workflows, LLM-based systems, and the engineering principles required to build AI agents that are scalable, observable, and fault-tolerant. I am interested architecting production AI systems, including state management, resilient workflows, tool orchestration, evaluation, reliability, and designing systems that can continuously adapt and evolve.