Seldon

Seldon is a leading MLOps company enabling enterprises to deploy, monitor, and govern machine learning models at scale. I led the UI design of the company’s core SaaS platform, working at the intersection of ML infrastructure, DevOps practices, and enterprise software.

The platform served data scientists, ML engineers, and platform teams in Kubernetes-based environments — deeply technical users with very limited time and high demands for clarity and reliability. My role was to shape how complex ML workflows were understood and navigated inside the product.

Machine learning infrastructure is inherently complex. I reduced cognitive overload through progressive disclosure for advanced configuration, multi-step deployment flows with clear validation states, better visibility of system status, logs, and model health, and clearer visual hierarchies for inference graphs and pipeline components.

I redesigned and matured Seldon’s core design architecture — consistent components, structured layout principles for dense data, reusable Figma assets, and alignment between brand and product. As part of the design leadership I mentored designers, introduced critique rituals, and raised quality benchmarks across interaction, accessibility, and consistency.