About
Innovative engineering executive with 16+ years of experience, currently leading Amazon's Intent Personalization org of 75+ engineers and applied scientists. My teams deliver real-time large-scale personalized recommendations and systems that power Search, Homepage, Cart, and Detail Page experiences for 500M+ customers globally — driving high-impact engagement, contributing $12B+ in ARR in 2025, and accelerating Amazon's topline growth.
I define and drive the multi-year technical vision for customer understanding and personalized recommendation engines at scale, leveraging ANN-based retrieval, transformer-based rankers, and NLP to deliver nuanced product and intent understanding. My influence extends across 150+ engineers, scientists, and stakeholders in Search, Ads, and Home.
I excel at building high-performance AI organizations that combine cutting-edge innovation with operational excellence — mentoring engineers and managers, and fostering collaborative environments where transformative AI solutions can flourish.
Experience
- Lead a 75+ person Personalization Intent organization (engineering and applied science), setting strategy and delivering AI-powered shopping experiences across Amazon.
- Define the multi-year vision and roadmap for Amazon's recommendation and customer intent platforms, contributing over $12B in annual attributed revenue (2025, +120% YoY).
- Lead a high-performing organization of Principal Engineers, Applied Scientists, and Engineering Managers — driving technical strategy, organizational scaling, and operational excellence.
- Own Amazon's real-time customer intent platform (500K RPS / 45B daily transactions) powering personalization across Search, Homepage, Cart, and Detail Pages.
- Lead strategy and adoption of an AI agent platform enabling autonomous engineering workflows through persistent memory, multi-agent orchestration, and secure integrations across Slack, Quip, Microsoft 365, and AWS — scaled across Personalization and expanded for multi-org use.
- Led development of LLM-generated customer profiles fusing deep-learning behavioral signals with semantic understanding to power last-mile ranking.
- Lead Amazon's next-generation AI recommendation platform, leveraging customer intent, embeddings, retrieval, and GenAI for explainable shopping guidance.
- Built an NLP/LLM-powered product intelligence platform standardizing product understanding and catalog attributes, enabling Search, Rufus, and p13n at scale.
- Established a new strategic org developing ML models and distributed infrastructure for Amazon's prompt-based conversational shopping experiences.
- Drive multi-year investment planning and cross-org execution across Search, Home, Ads, and Personalization.
- Transitioned from Principal Engineer to Engineering Manager in Q1 2020.
- Led 15 global engineers; led the implementation of a recommender engine using Google Vertex AI and custom embeddings.
- Built a multi-tenant real-time content personalization engine with multi-language and A/B testing infrastructure.
- Built event-driven microservices; orchestrated Kubernetes clusters for HA and failover, cutting downtime by 40%.
- Implemented gRPC-based microservices for low-latency communication, reducing request processing time by 50%.
- Developed distributed pricing engines, a personetics engine, deposit engine, and real-time alerts for the Digital Wealth team.
- Led backend development of an AI-powered student web filtering system, serving over 10M students globally.
- Introduced real-time content classification using NLP and ensemble-based URL scoring, cutting review delays by 70%.
Technical Skills
◆ Languages
✦ ML / AI
▲ Infrastructure
● Databases
★ Leadership
Notable Projects
Cross-Amazon Personalization Guild
Co-founded an internal guild that created shared libraries, best practices, and hiring rubrics across Amazon AI teams.
Real-Time Behavioral Personalization Layer
Built a near-real-time streaming pipeline (Kinesis + Flink + DynamoDB) that enabled session-aware ranking across Amazon.
Go deeper →
The systems, architecture, and technical decisions behind my work — written up in detail.