● Platform Leadership: Scaled messaging platform from Alpha to Enterprise GA, achieving 99.999% sync uptime and expanding throughput 16x (from 6K to 96K QPS).

● Core Architecture: Designed an Events Platform processing ~1B events/day and unified disparate dispatch paths into a single high-efficiency architecture.

● Infrastructure & C++: Built a high-performance frequency capping system in C++ using distributed key-value stores for subscriber-level probability calculations.

● Compliance & Security: Led technical audits for ISO 27001, SOC2, and RBI SAR, unblocking 95% of migration eligibility through AES-GCM encryption and Unified API delivery.

● Currently a part of the new Kindle Shorts project within Kindle Vella, with specific focus on refining success metrics and building necessary tooling to monitor and analyze those metrics by key stake holders.

● Monetization Infrastructure: Built WhatsApp template infrastructure and a 1:1 Sender Service using factory patterns to enable outbound marketing at scale.

● System Optimization: Optimized the financial data pipeline, reducing execution time from 13 hours to 2.2 hours and cutting incident response time by 65%.

● Leadership & Handoff: Led a 10-engineer team as Tech Lead for a financial reconciliation platform and managed a multi-month product ownership transition.

● Engineered an AI-assisted on-call application that automates UI error detection and summarization; adopted by multiple orgs to accelerate incident response and bug-fix cycles.

● Designed and led the team in creating a user-friendly application allowing publishers to analyze their data such as reads, likes, royalties, and bonuses earned for episodes and stories.

● Designed and implemented a feature that enabled authors to download & access their audit reports.

● Collaborated and built alignment across multiple teams to establish a seamless workflow for authors, enabling them to publish their stories effectively using Kindle Vella.

● Enhanced reader experience by implementing various sorting mechanisms, including category and interest filters, to ensure stories are more visible and tailored to reader preferences.

● Currently a part of the new Kindle Shorts project within Kindle Vella, with specific focus on refining success metrics and building necessary tooling to monitor and analyze those metrics by key stake holders.

● Successfully established multiple deployment stages - development, staging, and production environments - ensuring a well-structured and reliable deployment process.

● Have an active role in an on-call rotation to ensure continuous and uninterrupted experience for authors and readers for kindle vella.

● Part of the GlobalSearch Team which owns all the search backends across rivian.

● Worked on developing customer facing API’s for global search Application, Graphql, Spring boot Java and elastic search

● Implemented various features such as

● Most Recent clicks using Redis Cache.

● Vehicle Search API

● Order Search API

● Customer search API

● Setup the CI/CD pipeline for Global Search Application using Docker and Kubernetes.

● Setup multiple stages of deployment dev, staging and production

● Developed and built MigoAdmin Rest APIs using NodeJs and Typescript allowing the product team to perform CRUD operations of new regions, products and services.

● Thrashed user interaction time to perform CRUD operations by using MigoAdmin Service by 90%.

● Developed an analysis tool to process large scale data present in redshift using nodejs to compute different sets of results based on different criteria and stored the results in S3 for future reference.

●Reduced user interaction time by 80% by creating a data pipeline to pull data from MixPanel using redshift and AWS Glue.

● Developed a tool to track session activities of users and store information in a database for the analysis team in Java to analyze the trading pattern of customers.

● Managed team in designing and developing SQL Database Server in Ultimatix & created technical design database document.

●Analysed trading datasets using Hadoop Map Reduce, Hive, Pig, Mahout, Amazon EMR to find which application was used most for trading.