Software Development Engineer

I started my career in backend development, building APIs, database-driven applications, and software that solves real business problems. As I gained experience, I became increasingly interested in AI and how it could make software more useful, which led me from traditional backend engineering into RAG systems, multi-agent workflows and AI-powered automation.

What makes my journey unique is that I have worked across both sides: building the backend systems that applications depend on and designing AI workflows that make them more capable. For example, with Scraiquill, I redesigned an AI content pipeline from 13 to 17 sequential LLM calls into a four-agent workflow with quality checks and automatic recovery. This reduced LLM calls by approximately 55%, lowered token consumption by 69% and improved output accuracy by 25%.

I have also worked on SaaS products, third-party integrations, database optimization, and deployment, which has taught me to think beyond writing code. I care about how a system performs, how it fails, what it costs, and whether it actually solves the intended problem.

I would describe myself as an engineer who enjoys understanding the bigger picture, learning by building, and taking ownership of turning ideas into reliable software