I’m a software engineer and technology leader with 14+ years of experience building and evolving large-scale distributed systems, enterprise platforms, and AI-powered products across marketplace, fintech, payments, SaaS, identity, and data domains.

My work sits at the intersection of architecture, engineering execution, and product impact. I’ve modernized platforms handling 200M+ requests a day, led AI-powered support automation serving a meaningful share of customer interactions, improved checkout conversion, built financial and identity systems, and helped teams navigate complex technical decisions where reliability, scale, and customer experience all matter.

What interests me most is not technology in isolation, but the decisions behind it: How should a system behave when dependencies fail? Where should intelligence live? What should remain deterministic? When does a platform need to evolve rather than simply scale? These are the kinds of problems I enjoy working through.

More recently, I’ve been exploring how LLMs, retrieval systems, agentic workflows, and traditional distributed architectures can work together in enterprise environments. I’m particularly interested in AI systems that are useful beyond the demo—systems that are observable, grounded in evidence, resilient to failure, measurable, and designed with clear human oversight.

My experience has taken me through several perspectives on engineering: individual contributor, technical lead, engineering manager, and startup founder. That breadth has shaped how I approach problems. Architecture matters, but so do product outcomes, operational simplicity, team ownership, and the ability to explain why a particular decision is the right one.

This website is where I document that thinking—through projects, architecture explorations, and writing about the hidden systems and engineering decisions behind the software we use every day.

What I focus on

• Distributed systems & platform architecture — resilient services, APIs, event-driven systems, scalability and modernization.
• Enterprise AI engineering — LLM integration, RAG, orchestration, evaluation, guardrails and human-in-the-loop systems.
• Product-minded engineering — connecting technical decisions to reliability, customer experience and measurable business outcomes.
• Technical leadership — turning ambiguity into architecture, execution plans and systems that teams can sustainably own.

I believe the strongest engineering is rarely about adding more technology. It is about understanding the problem deeply enough to know what belongs in the system—and what doesn’t.