About
I sit at the intersection of enterprise software and applied AI — turning ambiguous business problems into systems that survive production.
I started writing production software in 2014, and I have spent the eleven years since inside the machinery of real businesses: CRM pipelines, field service dispatch, compliance workflows, billing systems, and the integrations that hold them together. That work taught me the skill I value most — listening to how a business actually operates, then designing software that fits it rather than fighting it.
In enterprise SaaS product teams I have led engineering behind property technology ecosystems: multi-tenant SaaS platforms, Microsoft Graph and Stripe integrations, event-driven backends, and — since 2023 — AI systems in production. I have shipped RAG pipelines that process tens of thousands of documents a month, AI assistants grounded in enterprise data, and agent workflows with humans in the loop where it matters.
My mission is simple: close the gap between what AI and modern infrastructure make possible and what enterprises actually deploy. That gap is where forward deployed engineers live, and it is exactly where I do my best work — embedded with users, accountable for outcomes, fluent in both the boardroom problem and the pod that is crash-looping.
11+ years in enterprise software
3 years shipping AI systems to production
25+ integrations — Microsoft Graph, Stripe, SendGrid, Google APIs
6 engineers mentored and led
Full lifecycle — discovery to incident response
Timeline
2014
First production systems: web apps, REST APIs, payment integrations.
2016
Led modules of a CRM platform serving 200+ sales users.
2018
Docker, AWS, CI/CD pipelines became core to every delivery.
2019
Senior engineer on enterprise proptech and field service platforms.
2021
Outbox pattern, message queues, and distributed workflows in production.
2023
First LLM features in production: document extraction and semantic search.
2024
Shipped RAG pipelines, AI knowledge assistant, and agentic workflows.
2026
Deep in MCP, LangGraph, multi-agent systems, and enterprise AI delivery.
Values
Four principles that show up in every system I build and every team I work with.
The best architecture is the one that solves the customer's actual problem. I start from the workflow, not the tech stack, and validate with users early.
A demo is a promise; production is a contract. I design for observability, failure modes, and the 3 a.m. incident from day one.
I reach for boring, proven technology first and add complexity only when the numbers demand it. Every abstraction has to pay rent.
Discovery, design, implementation, deployment, and the follow-through afterwards. Forward deployed means being accountable for outcomes, not tickets.
I lead by shipping alongside the team: design reviews that teach, code reviews that raise the bar, and incident retros without blame. Since 2023 I have driven our AI adoption — starting with narrow, measurable wins (document extraction, semantic search), building the eval infrastructure to trust them, then expanding into RAG assistants and multi-agent workflows. I believe the next decade belongs to engineers who can deploy AI into messy enterprise reality, and I have organised my career around being one of them.