Jul 2017 – Oct 2019 · 2 yrs 3 mos · Palo Alto, California and Bangalore

• Worked remotely from Bengaluru, India from April 2019
• Developed software libraries in Python/C++ to interact with a wide variety of equipments like digital and analog I/O devices, DAQ’s, power supplies etc. utilised in the measurement and control of Tesla modules.
• These equipments were utilised in the functional evaluation of Low Voltage automotive modules at the contract manufacturing plants
• Implemented a new Python/ReactJS based software framework using micro services architecture for end-to-end functional evaluation of Tesla’s low voltage modules like Media Control and Autopilot module for Model SX and 3.
• Designed and wrote scripts in Python to functionally evaluate various interfaces of the modules under evaluation according to provided specifications
• Supported the deployment and management of evaluation software at contract manufacturing via continuous integration and delivery, both offsite and onsite.

Aug 2016 – May 2017 · 9 mos · West Lafayette, Indiana

• Instructed a Information Systems and Distributed Class
• Instructed a lab session of 30 students, preparing teaching material and grading homeworks
• Prepared SQL, JDBC and PL/SQL tutorials, created a Spark project and handled environment setup

Aug 2015 – May 2016 · 9 mos · Lafayette, Indiana Metropolitan Area

• Worked on a web-based platform used for data analysis and exploration.
• Developed database technologies and infrastructure for collecting, analyzing and mapping data.
• Created medical and scientific research databases using the infrastructure.
• The infrastructure is built as CMS PHP components on HUBzero, a web-based platform.
• Implemented JS/PHP survey forms, Postgres tables and jQuery data viewers

May 2016 – Jul 2016 · 2 mos · Sunnyvale, California

• Worked on gesture recognition of golf shots from sensor data
• Collected and analyzed gyroscope and accelerometer data from a network of wearable sensors placed on a golfer's body when he plays golf shots
• Used time series semi-supervised techniques to build and extract features for training data, and improve upon it using DTW-D distance
• Classified unknown data samples with a 90% accuracy using N-dimensional Dynamic Time Warping, implemented over a C++ standalone environment

Jul 2012 – Jul 2015 · 3 yrs · Greater Bengaluru Area

• Promoted to Software Engineer II in a span of 2 years
• Project: VMware vCloud Connector
• Involved in the end-to-end product development of vCloud Connector, a VM migration tool across datacenters
• Implemented the path-optimized primary VM migration workflow between the source and destination data centers with features like leveraging upon HTTPS/UDT protocols based upon inter-cloud network conditions, and data buffering for handling network congestion and different read/write speeds, increasing transfer speeds by 50%
• Designed and wrote a feature to large VM's in a fragmented and encrypted format onto a disk shipped to customers for faster migration
• Developed a product feature that replicates VM templates across multiple data centers/clouds and gives the ability to sync VM data across them
• Orchestrated the build infrastructure for new version releases, and applied security patches.
• The backend features were implemented using JAVA with a PostgreSQL and MongoDB database with a Hibernate and Liquibase access layer
• The middleware for UI and backend interaction was written in Python
• Project: VMware vCloud Air Plugin
• Created a web plugin for VMware’s public cloud offering, the vCloud Air (vCA) to provide the customers a way of accessing core vCA features and resources for their on-prem data centers without the complexities of the web interface
• The UI component was implemented in HTML/Javascript using frameworks like ExtJS and AngularJS and backend adapters were coded in Java to talk to vCA stack
• Developed an on-prem caching solution to improve performance by optimally storing vCA inventory in a database

May 2011 – Jul 2011 · 2 mos · Greater Bengaluru Area

• Prototyped a software analyzing and rating sentiments of user comments on IMDB media contents like movie review and comments
• Utilized existing IMDB API’s and generic tools and techniques like Curl and Regex to crawl and parse data
• Implemented a Naive Bayes Classifier using Hadoop and Google Map Reduce to calculate user sentiments from the collected data, and provide a rating for the movie based on the sentiments.
• Achieved a 90% accuracy rate of the ratings provided by us in comparison to the user provided ratings on these contents