Member of the Machine Learning Platform team that owned a custom Feature Store, an internal service framework leveraging FastAPI, client/catalog data pipelines, and support for data science teams deploying and scaling recommenders in production.
Collaborated with a small team to design a new system in AWS built primarily with Python, Redis & Go (golang) to replace a legacy, memory intensive Python+Pandas candidate filtering application. Primary goals: scalability, flexibility, modularity, decreased latency and memory efficiency.
Migrated key legacy services reliant on offline data generated daily to use real-time, low latency client and inventory data.
Facilitated merchandising and product team requests to enable quick wins that increased inventory exposure and dramatically improved sell-through rates.
One of a few engineers to overhaul a high-throughput internal routing and configuration layer fronting hundreds of back-end Algorithms services in AWS.
Collaborated across teams to build a novel, performant mechanism providing detailed, up-to-the-second client information as JSON with a shared schema. The system enabled brand new realtime shopping features on Stitch Fix services.
