Jul 2023 – Jun 2025 · 1 yr 11 mos · Seattle, Washington, United States · On-site

• Promoted to SDE II for driving impactful technical initiatives and scaling systems that supported $100B+ in annual revenue across 3M+ AWS customers. I focused on building distributed platforms and event-driven architectures that enhanced AWS’s global sales planning experience.
• Key Contributions:
• Engineered a dynamic reporting and analytics platform using AWS Lambda and OpenSearch, powering 50+ reports accessed by 1,000+ planners each month.
• Designed and implemented real-time Salesforce–AWS integration layers using Kinesis Streams, EventBridge, SQS, and Apex triggers—reducing sync latency by 95% (from 3+ hours to under 5 minutes).
• Led phased migration to Salesforce, supporting seamless team adoption through robust data pipelines, reliable event processing, and fault-tolerant retries.
• Built an in-house engine to automate sales territory design and assignments, eliminating 60,000+ hours of manual work annually and enabling scalable parallel processing workflows.

Oct 2021 – Jul 2023 · 1 yr 9 mos · Seattle, Washington, United States · On-site

• As an SDE I on the Sales & Planning Platform team, I contributed to building core infrastructure and internal tooling to support AWS's global sales operations, revenue forecasting, territory planning, and account management.
• Key Contributions:
• Designed, built, and maintained 4+ distributed serverless microservices using AWS Lambda, DynamoDB, OpenSearch, S3, SQS, Step Functions, and EventBridge.
• Partnered with UX designers to build a scalable web app for managing 50K+ territories & 2M+ customer accounts—supporting advanced filters, rules, and insights.
• Led early-stage security design discussions with AppSec engineers and ensured all services met AWS's highest security standards.
• Mentored junior developers, contributed to team design reviews, and actively participated in the hiring loop for the broader org.

Jun 2020 – Aug 2021 · 1 yr 2 mos

• Engineered and implemented local, online location privacy algorithms (e.g., Laplace, Spatial Cloaking, VHC) in to sanitize real‑world location datasets (Geolife and RioBuses).
• Designed and developed privacy attack algorithms (inference and re‑identification attacks) to empirically evaluate the effectiveness of privacy preserving methods.
• Developed open‑source libraries for location privacy, enabling reproducibility and adoption by the research community.
• Published findings at SIGSPATIAL ’21, providing actionable insights for developers and researchers.

Jun 2019 – May 2020 · 11 mos

• Demonstrated that computationally expensive metaheuristic algorithms can run effectively on distributed VIFI infrastructure by programming Particle Swarm Optimization (PSO) and Ant Colony Optimisation (ASO) in Python3.
• Deployed a distributed infrastructure using Docker to train convolutional neural networks with alternative metaheuristic algorithms instead of gradient descent.

Jan 2019 – May 2019 · 4 mos

• Teaching Assistant for Machine Learning Spring '19.
• Developed and evaluated quizzes, programming assignments, and exams for a class of over 100 graduate students.

Aug 2018 – Dec 2018 · 4 mos · Charlotte, North Carolina Area

• Implemented AlexNet based deep learning model to classify stains on industrial building surfaces for autonomous cleaner drones.
• The trained model identified stains on brick surfaces with 96% F1 score on training set, and 76% on test set.

Aug 2017 – Apr 2018 · 8 mos · Pune Area, India

• Developed a framework in C++ using OpenGL 3.3 to perform 3D rendering of molecular data stored in high performing Chemical Markup Language (CML) models.
• The new framework made model ingestion and visualization 250% faster and enabled trajectory and rotation analysis of complex amino acids.

Jun 2017 – Aug 2017 · 2 mos · Rome Area, Italy

• Built an application to identify the approximate number of people in the room using LTE signals.
• Resulting Bayesian model had an accuracy of 81% and a localization error of 35%.