AI engineering
Retrieval pipelines end to end — ingestion, chunking, embeddings, vector search, and LLM response generation. Agents and tool calling for multi-step automation.
Five years building AI-powered products, mobile apps, and web platforms — as the engineer writing the code and the lead getting it shipped. Currently leading a team of five across three concurrent products on a weekly release cadence.
Retrieval pipelines end to end — ingestion, chunking, embeddings, vector search, and LLM response generation. Agents and tool calling for multi-step automation.
Cross-platform and native applications from architecture through store submission, compliance review, and staged rollout. Fifteen shipped to production.
Full-stack product work across enterprise SaaS — frontend interfaces, REST API design, data modelling, and third-party service integration.
Products I've built and delivered, as engineer, technical lead, or both. Client work — code is private, but I'm happy to walk through architecture and decisions.
Projects I own outright, with source you can read.
Upload a PDF, ask questions against it. Full retrieval pipeline with no framework abstraction: chunking, embeddings, similarity search, and context assembly written directly against the provider SDK.
A small, clean Flutter codebase showing structure, state management, and API integration the way I'd set up a production project.
What I reach for, roughly in order of how often I use it.
I work async-first across time zones and have done since 2021. If you're hiring for AI, mobile, or full-stack engineering, I'd like to hear about it.