01 About
For 12 years I've built production software, machine learning, and data systems across retail, consumer goods, hospitality, finance, education, and healthcare, the last 9 embedded with enterprise teams. I started in mobile and payments apps, moved into applied data science, and now work mostly on LLM agents.
I also teach what I build, running hands-on workshops on LLM agents and AI coding tools.
I care about pragmatism: AI that actually changes decisions, not demos.
02 Latest
LLM agents. The model interprets what a person means; a deterministic service decides what may happen. Tools are generated from a signed capability catalog, refusals come back as structured notes the agent can act on, and every stated fact is checked against the source system before it reaches a person.
Evaluation. Simulated-user scenarios, hard-bar safety invariants, deterministic graders against the tool-call log, and model triage across providers.
Production ML. Recommender systems, from collaborative filtering and learning-to-rank to two-tower retrieval, and deep-learning demand forecasting.
Research. An independent line of work on latent predictive representations and graph neural networks.
03 Experience
- 2025–now
LLM agents in a regulated enterprise setting: a transactional agent taken from kickoff to a controlled pilot in under three months, with evaluation and grounding built in.
- 2023–2024
Data platforms and recommender systems: a customer 360 built from scratch on Azure and Databricks for a hospitality and entertainment group, and B2B recommendations for a consumer-goods company.
- 2018–2023
Applied data science: deep-learning demand forecasting, recommenders, churn and segmentation for retail, finance, and education.
- 2014–2018
Software engineering and architecture: native Android and iOS apps, highly scalable payment systems (Java, Kotlin, and Go), and tech lead of delivery squads.