Apr 2020 – Aug 2020 · 4 mos

May 2019 – Mar 2020 · 10 mos

Jan 2019 – May 2019 · 4 mos · Bengaluru Area, India

• Developing Micro-Services to solve complex problems in e-commerce domain
• Technologies used: Java, Dropwizard, AWS Services, Redis, Kafka, MySQL, Maven, Git
• ▪Designed and developed a generic wrapper for Redis Cache and extended it to cache most used business entities
• ▪Designed and developed a generic framework to create Target Grouping of various entities for the campaign and offer application
• ▪Developed and integrated incremental feature enabling field executes to perform additional tasks apart from delivery on daily operations
• ▪Identified hibernate query performance bottlenecks and improve their performance by reducing the number of n+1 queries

Jul 2016 – Dec 2018 · 2 yrs 5 mos · Bengaluru Area, India

• RailMax, a Real Time Scheduling System for Rail Operations
• ▪ Developed generic Undo Framework using Delegates by which any collection can be trackable (Undo / Redo)
• ▪ worked on extending Undo framework to develop schedule level undo features for RailMax Product
• ▪ Worked on migrating entire backend from Unity framework to Dapper ORM / Protobuf and integrated it to Desktop Client
• ▪Worked on Application Cache to improve performance of entire application which was received extremely well by clients
• PortMax, a Weekly-Schedule Optimization Tool for port operations
• ▪Responsible for requirement gathering and played key role in successful delivery of the phase-2 of the project
• ▪Developed a multithreaded framework and used it to implement Neighbourhood Search Optimization Algorithm
• ▪Developed an approximate metaheuristic algorithm to get optimal solutions faster than existing approach
• ▪Designed and Implemented a greedy product assignment algorithm which improved schedule quality significantly
• ▪Developed testing module in T-SQL and automated the testing and deployment process with integration to TeamCity and Octopus

May 2015 – Jun 2015 · 1 mo · Mumbai, India

• Prediction of adulteration of milk using Machine Learning
• ▪Applied Noise Correction, Filtering, and Dimensionality Reduction techniques on raw train spectrum data with 1481 wavenumbers
• ▪Executed advanced Machine Learning Algorithms like GBM, SVM, RF, GLM etc. to predict the concentration of ‘analyte’ in Milk
• ▪Result: An Ensemble Model of above algorithms to achieve 95.2% prediction accuracy on unknown test data of 21 samples
• Content Filtering and Clustering using Natural Language Processing
• ▪Implemented Web-Crawling and Scraping program for extracting live news feed from 10 different online news portals and blogs
• ▪Extracted Named Entities and money figures involved in article and built an algorithm to rank each article based on its content
• ▪Result: An interactive prototype of web application hosted on shiny cloud service to display results of 200 news articles