Jeena Thampi

About Jeena Thampi

I’m a Software Engineer II at Amazon Lab126, working on Alexa Reminders, a customer-facing domain responsible for creating and managing reminders across all Alexa devices at large scale.My work focuses on building distributed backend systems using Java and Spring MVC, backed by AWS-based infrastructure, with an emphasis on correctness, reliability, and scalability. Many of the systems and features I’ve worked on operate at millions of monthly active users, which has shaped how I think about data modeling, validation, error handling, and production readiness.As part of Alexa+, I’ve also contributed to LLM-powered customer experiences, including prompt engineering and LLM-based optimization/tuning, applying AI in production systems while maintaining strong guarantees around user experience and system behavior.Previously, I interned at Amazon Lab126 on Alexa Reminders, where I designed and implemented a Reminder Detail View feature launched behind a feature flag and rolled out from beta to general availability. I also interned at Intuit on QuickBooks Online Payroll, contributing to the modernization of legacy UI by shipping production-ready components.I’m particularly interested in distributed systems at scale, building customer-facing platforms that are resilient, observable, and easy to evolve.Tech: Java, Spring / Spring Boot, AWS, DynamoDB, distributed systems, applied LLM workflows, prompt engineering

Experience

1. Software Development Engineer Ii

   Amazon Lab126

   Feb 2022 — Present · CA, US

   Team overview:Working on Alexa Reminders, a core Alexa domain responsible for creating, managing, and delivering reminders across all Alexa devices in the Alexa ecosystem. The domain operates at large scale, serving millions of monthly active users, and owns highly reliable, customer-facing distributed systems.Key contributions & responsibilities:Designed, built, and maintained distributed backend systems powering Alexa Reminders, with a strong focus on scalability, correctness, and operational reliability at high user volumeDeveloped production features using Java and Spring, backed by AWS-based infrastructure, with careful attention to data modeling, validation, error handling, and backward compatibilityWorked on customer-facing reminder experiences across Alexa devices, collaborating closely with Product Managers and UX Designers to translate business requirements into scalable technical solutionsContributed to Alexa+ initiatives by building and integrating LLM-powered capabilities into existing systems, including:Prompt engineering to improve response quality and consistencyLLM-based optimization and tuning to enhance user interactions while maintaining reliability guaranteesApplied AI capabilities in production environments, ensuring that LLM-driven features met Alexa’s standards for correctness, latency, and user experienceLed integration efforts across multiple internal services and components, coordinating dependencies and ensuring smooth end-to-end feature deliveryActively participated in design reviews, code reviews, and operational readiness reviews, influencing system design decisions beyond individual featuresMentored junior engineers and interns, providing guidance on system design, code quality, and Amazon engineering best practices, and supporting their onboarding and growth within the teamTech stack: Java, Spring / Spring Boot, AWS, DynamoDB, distributed systems, applied LLM workflows, prompt engineering

Education

• San José State University

  Master's degree, Computer Software Engineering

  2020 — 2021

• Government Engineering College, Barton Hill, Thiruvananthapuram

  Bachelor of Technology (BTech), Information Technology

Skills

• Java Enterprise Edition
• Pega Prpc
• C++
• Javascript
• Mysql
• C
• Unix
• Microsoft Sql Server
• Software Development
• Java
• Websphere Application Server
• Oracle
• Html
• Sql
• Linux
• Xml