Software engineer · product · infra · applied AI systems
Product-minded software engineer specializing in applied AI, distributed systems, and backend infrastructure. I turn ambiguous product ideas into production systems, then own the architecture, outcomes, and reliability as usage scales.
2024 — Present · Intuit
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.
2023 · AnimePics
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.
2018 — 2023 · Ethos
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.”
2015 — 2018 · Curology + hiQ Labs
Started professionally in frontend and product engineering, then moved deeper into backend systems after building a stronger computer science foundation.
Built
to ship
and scale.
Consumer GenAI product with custom model training, GPU orchestration, payments, uploads, and email fulfillment.
Reliable service communication through outbox, CDC, schema validation, and Kafka.
AI expert matching system that scaled to 1M+ yearly interactions and delivered $25M+ in business impact.
Hosted 20+ contact-center ops agents behind one runtime for tools, auth, sessions, and enterprise workflows.
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.
Hot take - AI makes working with bad software engineers much more painful. Bad engineers were somewhat rate-limited.
"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.
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.
A lot of the decision making process now disappears into a black hole on Anthropic's servers. With LLMs, you often see the final implementation but lose the reasoning that produced it. That reasoning trail can be super valuable.
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.
A software engineering truism that no longer makes sense after AI codegen: "Never do a full rewrite."
A few years ago I wrote down advice for fixing slow SQL queries. Today, that kind of writing should probably be turned into a skill.
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.
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.
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.