Software engineer · product · platform/infra · applied AI systems
I work where product ambiguity turns into backend systems, platform infrastructure, and applied AI. The through-line is ownership: understand the business problem, design the system, ship it, and keep it running.
Experience
Built AI-powered marketplace, recommendation, and workforce optimization systems for Intuit's Virtual Expert Platform. Worked on production applied-AI systems that improved routing coverage, operational onboarding, and customer-support outcomes at large scale.
Built a GenAI product from scratch that transformed user photos into anime illustrations. Owned the business, product, and engineering, including custom model training, payments, authentication, async GPU jobs, and media delivery.
Joined near the beginning and helped build the platform foundations behind a fast-growing engineering org: Kubernetes, deploy automation, Postgres migrations, eventing, service boundaries, and production operations. Helped scale Ethos from Series A to 100+ engineers, $50M ARR, and a multi-billion-dollar valuation.
“Eric was one of our first engineers… He played a fundamental role in building major parts of both our frontend and backend which have grown to become our core systems.”
Started professionally in frontend and product engineering, then moved deeper into backend systems after building a stronger computer science foundation.
Founder-led GenAI product with custom model training and async GPU delivery.
PlatformReliable service communication through outbox, CDC, schema validation, and Kafka.
Applied AIProduction applied-AI system that improved routing coverage, operational onboarding, and support outcomes.
AgentsShared agent platform for production workflows across state, tool execution, rollout, and observability.
A software engineering truism that no longer makes sense after AI codegen: "Never do a full rewrite."
A software engineering truism that feels weaker now after AI codegen: "The best programming language is the one your team currently uses."
A software engineering truism that feels weaker now after AI codegen: Brooks's Law from The Mythical Man-Month.
Simple beats clever. Duplication beats the wrong abstraction.
AI has let people develop systems that cannot be managed or reasoned about without the assistance of AI, creating a lock-in effect and a dependency on the models.
"Tech debt" is actually a great name because debt is not always bad. Sometimes it is good.
Maybe a hot take here - I think AI codegen is reducing the value of platform teams. Specifically those that have centralized platform ownership.
So one great side effect about machines that can process a Harry Potter novel amount of information in seconds is they can make debugging a lot quicker.
Have you ever talked to an engineer about their PR and you can tell they have no idea what is in it?
You ship a feature that queries the DB and brings a lot of data into memory. There is a separate thread that dequeues and processes logs from a queue in a loop.
Hot take - AI makes working with bad software engineers much more painful. Bad engineers were somewhat rate-limited.
The reason why I think bad AI writing bugs people so much - it is genuinely disrespectful to ask someone to put effort into reading something you couldn't be bothered to put an ounce of effort into writing.
LLMs have definitely made software development less collaborative. Personally I used to get pinged all the time for advice on code structure, technology choices, architecture, and so on.