Building the infrastructure
behind intelligent systems

I am a backend engineer focused on designing the systems that power modern AI and data-driven applications. My work sits at the intersection of distributed systems, data platforms, and applied AI, with a strong emphasis on understanding systems from first principles rather than treating technologies as black boxes.

Currently, I work in the conversational intelligence domain, building systems around audio data, analytics, and AI-driven workflows. My experience spans backend engineering, data-intensive systems, and platform architecture — particularly in areas involving large-scale data processing, storage, and analytics.

I am deeply interested in modern data infrastructure and lakehouse architectures — especially Apache Hudi, Spark, Hive Metastore, CDC pipelines, analytical query engines, and object storage systems. I enjoy designing systems that bridge transactional and analytical workloads, enabling scalable and reliable data movement for AI and analytics applications.

Beyond data engineering, I actively explore distributed systems, database internals, RPC frameworks, and AI infrastructure. Strong engineering comes from understanding fundamentals deeply — which is why I spend significant time studying database systems, distributed computing, GPU computing, vector databases, and AI agents.

I am also building expertise in applied AI systems, including Retrieval-Augmented Generation (RAG), agentic workflows, model evaluation, and conversational intelligence platforms. The long-term goal: design systems where data, infrastructure, and intelligence work together seamlessly to solve real-world problems — at meaningful scale.