Experience

Four jobs. Highlights first. Open a role for the writeup.

Back to site Resume

2026 — present

Chiang Rai, remote

College Professor

HAIT

The IT, the public site, and the first AI class.

Runs hosting and security for the school.
Rebuilt the public site and the PHP under it.
Wrote the first AI curriculum and taught the first cohort.
Set up OpenClaw and OpenAgent workflows, and a data-recollection project with someone at Korea University.

Writeups 4 projects

Public site The public site is up.

PHPHosting

Rebuilt the front-end and upgraded PHP.
Fixed the hosting so the pages load.

Problem: The school needed a public site that loaded cleanly.
Constraint: We kept the host they already had.
Decision: Redesigned the front-end, upgraded PHP, and fixed the hosting config.
Outcome: The site is live.

haitedu.com

Institutional IT Runs hosting and security for the school.

HostingSecurity

Runs hosting and security.
The site is up.

Problem: The school needed someone on hosting and security.
Constraint: The public site was going live at the same time.
Decision: Took hosting and security.
Outcome: Hosting and security are still covered.

AI curriculum Wrote the first course and taught it.

Wrote the first AI course.
Checked the lab machines, then taught the class.

Problem: They needed a first AI course and someone to teach it.
Constraint: First cohort. The lab machines were checked first.
Decision: Wrote the course, checked the machines, and taught the class.
Outcome: The first students finished the course.

Multi-agent orchestration OpenClaw and OpenAgent for the school, plus a recollection project with Korea University.

OpenClawOpenAgent

OpenClaw and OpenAgent, set up so students and staff can run them.
A separate recollection workflow with a collaborator at Korea University.

Problem: The school wanted agents students and staff could run, plus a data project with someone at Korea University.
Constraint: Everything had to run on the lab machines.
Decision: OpenClaw and OpenAgent on the teaching side. A separate recollection workflow with that collaborator.
Outcome: Students and staff use the teaching agents. The recollection project is drawn up.

Sep 2025 — Aug 2026

New York

Founding CTO

Stealth fintech

Kraken futures. Ingest under 100ms.

Built a trading engine on Kraken crypto futures. Python and Kafka, under 100ms from feed to book.
Agents that check portfolios against the SQL ledger.
Owned the models: PyTorch time-series, retraining around the clock, news processors for sudden moves, and a daily pre-market brief that cut the morning analysis by 90%.

Writeups 4 projects

Kraken futures ingestion Python and Kafka on the Kraken feed, under 100ms.

PythonKafka

Millions of ticks. Under 100ms end to end.
Python and Kafka on the official feed.

Problem: A live feed into the trading book.
Constraint: Kraken crypto futures, under 100ms.
Decision: Python and Kafka on the official feed, then agents on top of that book.
Outcome: Ticks come in. Orders go out.

Forecasting and retraining PyTorch models that retrain around the clock.

PyTorch

Time-series models for live signals and backtests.
A retraining job that runs all day, plus local training on a Mac.

Problem: They wanted signals that kept up with the market.
Constraint: Training had to keep going through the day.
Decision: PyTorch time-series models, retraining all day.
Outcome: Live signals and backtests. The models retrain on their own.

News processors News text in, pivot flags out.

Same schedule as the models.
It reads live headlines and flags pivots.

Problem: Some moves show up in the news before they show up in prices.
Constraint: The input is live headlines.
Decision: A news processor on the same clock as the forecasting job.
Outcome: Pivot flags from headlines, next to the time-series path.

Portfolio audit and pre-market brief Agents on the SQL ledger, and a morning brief that took about 90% of the analysis off the desk.

PythonSQLLLMs

Agents that query the SQL and check portfolios.
A daily pre-market brief that cut the morning analysis pass by 90%.

Problem: The morning portfolio check lived in SQL.
Constraint: The models had to work with the ledger they already used.
Decision: Agents that query the SQL, plus a PyTorch brief before the open.
Outcome: They run the brief every morning.

May 2022 — Jul 2025

New York

Full Stack Engineer

Goldman Sachs

80% faster processing. Forward-deployed. 50+ internal teams.

Forward-deployment engineer — developers, traders, and lawyers on one reporting path, so the numbers stayed auditable and legally compliant.
Designed and shipped a beneficiary management system serving 10,000+ client accounts and cut data processing time by 80%.
Scaled Java and Kafka pipelines and REST APIs used by 50+ internal teams, cutting data-access latency by 15%.
Built a Selenium and TestNG suite that raised test accuracy 22% across hundreds of financial and compliance scenarios.
Stayed on trading, compliance, and market-data APIs after they shipped.

Writeups 4 projects

Beneficiary management 10,000+ accounts. Processing time down 80%.

JavaKafka

Designed and shipped the beneficiary system behind 10,000+ client accounts.
Cut data processing time by 80%. Stayed on it after launch.

Problem: More than 10,000 accounts needed a beneficiary system people could use.
Constraint: Compliance, and more than 50 teams reading the same data.
Decision: Java and Kafka, plus a REST API for accounts.
Outcome: Processing time dropped 80%.

Java, Kafka, and REST Pipelines and APIs used by 50+ internal teams. Latency down 15%.

JavaKafkaREST

Scaled Java and Kafka pipelines and REST APIs used by 50+ internal teams.
Cut data-access latency by 15%.

Problem: Internal teams needed account data they could share over an API.
Constraint: More than 50 teams were on it, and the APIs had to stay up.
Decision: Same Java and Kafka pipelines, REST on /v1/accounts.
Outcome: Access latency dropped about 15%.

Forward deployment Developers, traders, and lawyers on one reporting path.

Sat between downstream developers, traders, and lawyers on the same reporting path.
The job was one set of numbers all three sides would sign.

Problem: Developers, traders, and lawyers all had to sign the same numbers.
Constraint: Three groups, one report.
Decision: Sat between the three groups and made one path.
Outcome: One set of numbers all three would sign.

Selenium and TestNG Test accuracy up 22%.

SeleniumTestNG

Built a Selenium and TestNG suite across hundreds of financial and compliance scenarios.
Raised test accuracy 22%.

Problem: KYC and compliance checks needed a real test suite.
Constraint: Hundreds of cases. The APIs were already in production.
Decision: Selenium and TestNG on those cases.
Outcome: Accuracy up 22%.

Oct 2020 — May 2022

La Brea, CA

Full Stack Software Engineer

One Dave Software

Curtis inventory. Defense apps. Email 70% faster.

Built inventory pipelines and frontends for Curtis, a government nuclear pipeline contractor.
Led a system-wide upgrade that cut email-server response time by 70%.
Built and maintained secure National Defense web applications.

Writeups 3 projects

Curtis inventory Inventory for Curtis, a government nuclear contractor.

Pipelines and the screens on top of them.
Built inside their contractor systems.

Problem: Curtis needed inventory tracking for a government nuclear contract.
Constraint: We were working inside their existing systems.
Decision: Built the pipelines and the frontends.
Outcome: They use it for inventory.

Email systems System-wide upgrade. Response time down 70%.

Managed email for the same clients.
A full-path upgrade that cut server response time by 70%.

Problem: The same clients needed faster email.
Constraint: The upgrade covered the whole mail path.
Decision: Upgraded the mail servers.
Outcome: Response time dropped 70%.

National Defense apps Web apps for national defense clients, with security as the starting point.

Built and kept full-stack apps for national defense clients.
Security review from the first ticket.

Problem: Defense clients needed web apps that stayed up and stayed locked down.
Constraint: Security review from the first ticket.
Decision: Built those apps and kept them running.
Outcome: They stayed in service.