Hi, I'm Sang.

A controls-testing platform that catches and escalates payment control failures across 52 of Citi’s global payment applications — checking ~25M records and roughly $1.5 trillion in payment value every cycle. I designed and built it alone: a config-driven Python system running four pipeline types — Threshold Validation, Duplicate Detection, Dual Blind Rekey, and GPOC executive reporting — each with its own business rules per application. It replaced a manual process that took over 150 hours a cycle and cut roughly 90% of that effort. The code is internal to Citi; the architecture and the outcomes are mine to discuss.

Internal tool — codebase confidential per Citi policy

A question-answering workspace for a team’s own files. Upload documents and spreadsheets, ask in plain English, and every answer comes back one of two ways: quoted, with the source documents listed underneath it, or computed — Avocado writes Python, runs it against your data in a locked-down container, and shows you the code behind the number.

It is careful about what it does not know. Asked what policies a workspace defines, it lists the three it found, flags that two of them are titles with nothing behind them, and closes with what is missing — the actual rules, effective dates and scope are not in these documents. Ask for an executive summary and it charts every spreadsheet in the workspace, then ends the same way.

Capabilities are switches rather than magic: each integration shows what it will cost in context before anyone turns it on. The sandbox is the part I would defend in review. Generated code runs with no network, a read-only filesystem, and hard memory and CPU caps — applied to every run, never set per request, because a caller that can choose its own limits can choose none. Every query is scoped to one workspace in SQL, so no team can read another’s data.

A service that reads a 200-page commercial credit agreement and returns the terms a credit team actually needs — borrower, facility amount, maturity, pricing, covenants, events of default — as structured data. Every value arrives cited, with the page and the exact sentence it came from. Each citation is then checked in code against the source text, so anything that cannot be traced comes back not found rather than invented.

Search runs in two passes: vector similarity over pgvector to gather candidates, then a cross-encoder re-ranker running locally to narrow them. Ingestion is queued through Kafka with an idempotent worker, so uploading the same document twice is safe.

Accuracy is measured, not assumed. An evaluation harness scores answers against hand-labeled keys — it took a real SEC filing from 57% to 71%, and it also disproved a change I expected to help, which turned out to make no difference at all.

A monitoring service that tracks three U.S. economic indicators — the federal funds rate, CPI and unemployment — pulling them daily from the FRED API, storing them in PostgreSQL, and raising an alert when a value moves further than its configured threshold.

I built it to close gaps I knew I had rather than to add another project. The whole environment is defined in Terraform — EC2 instance, firewall rules, key pair — so it stands up from nothing with one command instead of being a machine nobody dares touch. Deploys are gated on the test suite: the deploy job declares a dependency on the tests, so a failing push cannot reach production. And I ran a security audit before the first public deployment, which found six issues I fixed before anyone else could see them.

It ran live on AWS for five months. I took it down deliberately: the infrastructure code is the artefact worth keeping, and leaving a t2.micro running to serve a demo nobody is querying is a bill, not a portfolio piece. terraform apply brings it back.

An ELT pipeline that turns daily stock prices into a dashboard without anyone touching it. Every weekday at 6 PM Airflow starts the run: prices land in PostgreSQL from Yahoo Finance, dbt rebuilds a star schema across three layers (Bronze → Silver → Gold), 26 data quality tests check the output, and the Metabase dashboard picks up the result. The whole stack runs in Docker.

I built it to learn how data actually moves inside a team that does this properly, so it follows the medallion pattern rather than the shortest path from source to chart.

A full-stack PWA that helps parents track and support a child’s development: 12 feature modules, JWT authentication with refresh token rotation, a timezone-aware recurring event engine, and WHO percentile growth charts. I built it as a first-time parent who could not find one app that did all of it.

Clean Architecture across a 3-layer .NET 8 backend, a React 19 TypeScript frontend on Vercel, Docker multi-stage builds and GitHub Actions CI. It went through two full code reviews with a senior engineer — 25+ issues raised and resolved, including a 13-issue security audit before launch. This is the project that taught me what good engineering actually looks like.