I'm a backend engineer who ended up in data and AI because that is where the interesting failure modes are. I joined Datamaran as a junior in 2022 and grew into the senior role there, which in practice meant owning systems end to end: the pipelines that read 20,000 news items and 200 legal sources a day — one of them 85% cheaper to run than when I inherited it, the other saving the legal team 70% of its report-processing time — plus the auth and sharing layers underneath the product and the observability that tells us when any of it breaks.
I build and automate with AI in the loop — not as a demo, but as the way the work gets done. That means LLM agents wired into real pipelines (a scraper that repairs itself instead of paging someone), evaluation harnesses so a model or prompt change is an experiment with a number attached, and AI-assisted development spread across repositories and delivery processes.
The other half of the job is less visible and matters just as much: I pushed DDD, hexagonal architecture and TDD through the codebase, standardised linting and formatting, and fixed how we handle database connection pools. Clear layer boundaries, business rules in exactly one testable place, and failures that are loud and cheap rather than silent and expensive.