I started out as a developer working across C#, SQL Server and Python, gradually moving from building individual services to shaping platforms and teams. Most of my career has been in regulated financial environments where reliability, auditability, and speed have to coexist. I enjoy designing clear interfaces, automating the boring parts, and turning ambiguous ideas into small, testable releases. Over time I shifted from “project delivery” to “product thinking,” with an emphasis on data quality, reproducibility, and measurable outcomes. I still like to stay hands-on—reviewing code, writing tooling when needed, and removing friction so engineers can do their bestwork.
At Galytix I lead the engineering function behind our AI and data products. Our goal is to convert messy external data into trustworthy, explainable signals using strong metadata, ontology, andvalidation layers. We run on Kubernetes with clear SLAs, robust CI/CD, and first-class observability so that experiments graduate to production safely. Close collaboration with risk users and quants keeps us grounded in real-world impact rather than demos.
In finance, the next wave of advantage comes from disciplined engineering around data and ML—not only new models. I’m looking for product-minded engineers who can join the dots between platform, data quality, and user needs. At Galytix we’re hiring senior Python/ML engineers (FastAPI, Polars/Pandas, Spark), MLOps/platform engineers (Kubernetes, ArgoCD, Helm, observability), and specialists in data quality and credit risk. We also welcome partnerships with institutions keen to pilot our DQ Agent and RiskRadar and to co-design measurable, production-grade workflows.
“Ship small. Measure. Repeat.”