15+ years working on distributed systems, enterprise architecture, and platform modernization across cloud environments.

I have spent most of my career on established products with real scale problems. Systems processing millions of daily transactions, teams spread across multiple domains, and codebases carrying years of decisions that need careful untangling.

I work across the full stack but my focus is on architecture, platform engineering, and making sure the technical direction of a system matches where the business is going.

Backend

Frontend

SPAs Behavioral Analytics

Messaging and events

AI and LLMs

RAG Pipelines LLaMA 3.1 GPT-4o-mini AI Agents IoT

Vector and search

Vector Search Semantic Ranking Hybrid Search Embedding Pipelines

Data pipelines

LLM Orchestration Search Index Automation Document Ingestion

Relational databases

NoSQL and document

Cloud and DevOps

Observability

New Relic Splunk AppDynamics Application Insights Serilog Distributed Tracing

Distributed systems and architecture Breaking large systems into well-bounded services that teams can own and ship independently. The focus is always on reliability and operational simplicity, not just scale for its own sake.

Legacy modernization Taking monolithic platforms that have served their purpose and rebuilding them into something maintainable. This is less about technology choices and more about understanding domain boundaries and managing the transition without breaking what works.

AI integration Building RAG pipelines that connect document stores, search indexes, and LLMs into something genuinely useful. On one project this meant replacing a slow database-first document search with an Azure AI Search indexer across thousands of PDFs, then layering an LLM on top for summarization and chat. The interesting problems are rarely the model itself they are the ingestion pipeline, retrieval quality, and making the system behave predictably under real usage.

Platform engineering and observability Building deployment infrastructure on Azure and AWS, and making sure systems are observable from day one. Structured logging, distributed tracing, and alerting that actually fires when something goes wrong.

Good systems are not just technically correct. They are easy to change, honest about their own health, and designed around how the business actually works rather than how engineers wish it did.

The decisions that matter most are usually made early, around boundaries, data ownership, and observability. Getting those right makes everything that follows considerably easier.

I have worked across fintech, real estate, e-commerce, manufacturing, and education. The domains are different but the problems tend to repeat.