Working on developing robust and scalable graph database solutions.

Currently working on a project that scrapes Amazon catalog and tries to find potential information errors like cross-attribute mismatch, image mismatch via Deep Learning, MXNet and NER.1. Extended the whole architecture to support multiple clients with no client affecting the other client. This involved re-architecting the whole system for the new feature as well as to make it scalable.Technologies Used: Lambda, Step Functions, Kinesis Data Analytics, Kinesis Data Streams, Kinesis Data Firehose, Glue, Athena, SQS, SNS, S3, DynamoDB, Cloudwatch, ElasticSearch, Redis Cache.

Worked on developing and scaling a system named AutoASIN that auto-corrects vendor input based on a combination of historical data based learning and static business rules. Key technologies used are NLP, Hadoop, EMR and Graph-based supervised learning. Following are my major contributions to this project.1. Developed a mechanism to correct brand value shown on detail page via historic data.2. Developed a separate pipeline to unblock asin creation due to unnecessary errors. These items are then enriched after asin creation through manual intervention.Worked on a project that scrapes Amazon catalog and tries to find potential information errors like cross-attribute mismatch, image mismatch via Deep Learning, MXNet and NER.1. Developed a scalable reporting module for an intake of 100M events/day. This uses Firehose with Redshift to store the data such it is easy to build dashboards.Technologies Used: S3, DynamoDB, Lambda, Kinesis Data Streams, SQS, RPC, Cloudwatch, Redshift, Kinesis Firehose Streams.

Built an end-to-end infrastructure to provide insights into network monitoring data via visual dashboard on OBIEEE. This includes the following tasks:1. Consolidating all the data distributed across multiple nodes in different geographical locations to a centralized location which in our case was S3.2. Use Redshift to store this data into a format optimized for querying.3. Built OBIEE dashboard for visual capabilities.Technologies Used: Java, Distributed Job Scheduler, AWS S3, AWS Redshift, OBIEE.