status: building trustworthy systems

I build the infrastructure companies trust to run AI.

AI product engineering and infrastructure-as-code, for organizations that need real governance — not a demo.

01

before code, systems.

before i wrote a line of code, i studied how institutions work — universities, companies, the informal rules that hold an organization together when nobody’s watching. that turned out to be the right training for what i do now.

these days the hardest part of any real ai adoption was never the model. it’s the governance around it — who’s allowed to use it, what data it can see, what happens when it’s wrong. that’s a sociology problem wearing an engineering costume, and it’s the one i’m best at.

02

before frameworks, and after.

i started building interfaces before "frontend framework" was even a settled term — jquery, hand-rolled state, css that fought you every step of the way. then came the framework decade: angular end to end, enough react and vue to be dangerous, ngrx and redux for state that needed real discipline, component libraries and design systems instead of one-off pages, and tests, performance and accessibility that had to hold up in production — not just look right in a demo. i designed as much as i coded too — years of hands-on figma and adobe xd work built the instinct for what belongs in front of a user, even though these days that means reading a spec fluently, not drawing one, because a technically correct interface nobody wants to use isn’t actually done.

jquery era→framework decade→ai-assisted, now
03

What he runs now.

Frontend & Product Engineering

10 years — the foundation.

01

Systems Architecture

What you build AI on top of, if it’s going to hold up — system boundaries documented arc42-style, not diagrams that go stale after the workshop.

02

AI Product Engineering

Designing and shipping LLM-powered products — retrieval-grounded assistants and agentic workflows that get more accurate the more they’re used.

03

Infrastructure as Code

Provisioning and running the infrastructure AI products depend on — reproducible, auditable, built to survive an audit, not just a demo.

04

Governance & Sovereignty

Building the policy layer that lets a company set its own rules for AI instead of inheriting a provider’s.

04

The stack.

LLM & agent integrationRetrieval-augmented generationSelf-learning vector retrieval (Postgres)Policy & compliance engines for LLMsMulti-provider model routingOpenTofuBicep / ARMDockerKubernetesCI/CD pipelinesAzureSelf-hosted infrastructure (own cloud, Germany)Software architecture & documentation (arc42)TypeScript / JavaScriptReactAngularVueState management (NgRx, Redux, signals)Component architecture & design systemsMicro-frontend architectureTesting (unit, integration, e2e)Performance (Core Web Vitals)Accessibility (WCAG 2.1 AA)Node.jsDesign literacy (reads a Figma spec fluently)Technical leadership & mentoring
05

10 years, then now.

  • 01.11.2022 – todaySenior Software Engineer at Makonis
  • 01.10.2016 – 15.10.2022Full-Stack Engineer at Sascha Wolff
  • 01.06.2018 – 30.03.2021Co-founder of BreakfastClub U.G.
  • 26.02.2017 – 30.03.2021Co-founder of Kusuri Company
  • 01.10.2013 – 30.09.2016Master of Arts in Sociology
  • 01.10.2010 – 30.09.2013Bachelor of Arts in Politics and Society
06

why work with me.

i translate instead of picking one discipline and ignoring the rest: design and engineering, the old stack and the new one, technical depth and the people who actually have to live with what gets built. that’s the whole pitch.

get in touch — info@danielzenzen.eu
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