Robin Miklinski Independent AI product and systems engineer · London
I’ve spent fifteen years building software and leading engineers across AI products, global platforms, retail and defence.
Previously
I take AI products from prototype to release. The work spans architecture, data and integrations, then the evaluation, security and operating evidence required to put the system in use.
I make the product and engineering decisions, work directly in the code and carry the technical responsibility through release.
I turn manual hand-offs, disconnected tools and software that no longer fits the work into a system the business can rely on.
The answer may be an integration, a focused software tool, an AI-assisted step or no AI at all. I design and build the smallest change that removes the failure point.
I decide how the product should be structured, how data moves through it and how it connects to the software and services around it.
I turn product requirements and technical risks into concrete checks. For AI products, that includes evaluating outputs, testing misuse and failure cases, and deciding what evidence is needed before a release. I record what the testing did not establish alongside what it did.
I make the important technical decisions and work directly in the code. I work with the engineers already responsible for the product and leave them able to continue without relying on me. When a project needs deeper expertise in a particular discipline, I bring in the right specialist and keep the responsibilities and technical direction clear.
A non-destructive toolkit for reviewing, organising and exporting sample libraries, with previews, undo records and listening as the final approval step.
Source →A background audio service for metadata cleaning and BPM detection that leaves its source files unchanged.
Source →AI product evaluation. I created the evaluation and adversarial security testing for the company’s first customer-facing AI product. It covered answer quality, prompt injection, tenant isolation, personal data and retrieval scope.
Incident analysis. I built a Python pipeline across the company’s incident, documentation and monitoring systems. It analysed 451 production incidents over ninety days and supported a £227,000 annual business case for preventable failure.
Delivery infrastructure. I designed the systems coordinating roughly one thousand end-to-end tests across pull-request, release and nightly runs.
I also built and led the quality engineering function that operated these systems, growing it from zero to fifteen engineers across London and Sofia.
I took over a Dart, Flutter and Firebase product with little documentation, integrated a planning data API, restored a failed council dataset and rebuilt its scoring system. I then re-scored more than 24,000 planning applications and onboarded the external development team that continued the work.
I was Head of Quality Engineering at Reward Gateway. The role combined technical leadership with the delivery, evaluation and incident-analysis systems described above.
My experience spans defence and intelligence data systems, retail at peak scale, collaboration software and global SaaS used by millions. I work primarily in Python, JavaScript, TypeScript and C#, and I hold a BSc (Hons) in Internet Engineering from the University of Exeter.
Outside engineering, I DJ and produce electronic music as Eidetic. I have been a resident at EGG London and hosted Late Night Relics on AAJA Radio.
Tell me what you want to build, improve or understand. I’ll help shape the right approach and turn it into software that works in practice.
Prefer email? [email protected]