My work has evolved from enterprise delivery into product engineering: owning services, interfaces, data models, cloud workflows, production incidents, and the decisions that connect them.
The entries below focus on publicly documented product work rather than confidential internal detail.
Building enterprise applications and applied AI workflows, including Retail360 decision-support analytics, retrieval-grounded document Q&A, and agent-assisted pipeline monitoring. Public descriptions use reported scale and estimated impact while keeping internal architecture and data confidential.
Contributed to a microservice for the Pega platform, pairing enterprise application experience with Pega architecture knowledge and Oracle Cloud credentials.
Built an application that helped leadership review weekly operations through AWS Lambda, DynamoDB, and React. Owned high-severity incident response through root-cause resolution.
Developed scheduling flows for product maintenance updates using Spring Boot and React with TypeScript. Added data models that preserved change history for auditing.
Developed an Eclipse RCP environment for creating and running analytical functions, plus a Django microservice that installed those functions as cloud clusters were provisioned.
Implemented a Spring Boot calculation engine for on-demand insurance premiums and automated the application’s build process.
I connect user workflows to service boundaries, data design, cloud behavior, failure modes, and operational ownership.
I treat incidents as engineering information: stabilize, identify the root cause, fix the system, and preserve what the team learned.
I can move from a Java service and database model to a React flow or deployment concern without losing the product context.
Explore selected projects as concise case studies with the problem, contribution, stack, and engineering lesson.