Ethan

About Ethan

I build the infrastructure that makes AI work at scale — from real-time feature pipelines and ML platform infrastructure to payment systems, workflow orchestration, and agentic commerce workflows. Most recently at Snap, and prior to that almost 5 years at Amazon, I\'ve designed and operated cloud-native distributed systems on the critical path of ML platforms, checkout, payments, and product data.At Snap, I\'m currently building ML platform infrastructure and feature store systems — supporting user, document, and ads feature workloads at 2B-scale data volume and trillion-scale daily feature events, with CI/CD automation and orchestration for model-serving workflows at billion-scale prediction traffic. Recent work highlights:• Modernized Amazon\'s ASIN data platform from batch ETL to real-time streaming, reducing duplicate events by ~300× and delivering ~16× faster processing across ~10M ASINs — building AI-ready data foundations that enable ML pipelines, online inference, and Bedrock/SageMaker-backed applications at scale.• Led end-to-end architecture and delivery of checkout and payment capabilities for Buy with Prime, driving incremental orders/week and ~$500K–800K/year in rewards redemption — while laying the technical groundwork for payment agents and intelligent checkout systems.• Built a unified purchase workflow orchestration platform that raised observability from ~25% to >95%, reduced manual interventions by 4×, cut incident resolution by 6×, and lowered operational costs by ~$200K/year — moving complex operational journeys toward agent-assisted and automation-first execution. Alongside infrastructure work, I have hands-on production experience with GenAI: RAG pipelines, tool calling, agentic workflow orchestration, LangChain/LangGraph, MCP, guardrails, and LLMOps — applied to improve reliability, incident response, and engineering productivity across payment and commerce workflows.Core Skills- Java, Python, AWS- Distributed Systems, Backend Platforms, Workflow Orchestration- Real-Time Data Infrastructure, Streaming- Reliability Engineering, API Design, State Machines- AI/ML Infrastructure, Feature Stores, Online Inference, Model Serving, SageMaker, Bedrock- RAG, Tool Calling, LangChain / LangGraph, MCP, Agents, LLMOps, Guardrails

Experience

1. Sde Ii

   Amazon

   Jul 2021 — Present

Education

• Denison University

  Bachelor's degree , Mathematics

• Washington University in St. Louis

  Bachelor of Science, Computer Science