Shivendra Soni

Backend Developer at Saltside Technologies
Hapijs, JavaScript, Algorithms, NodeJS
Tools & Libraries:
Jenkins (Java), d3js, Neo4j, Apache Thrift (C++)
Indian Institute of Information Technology - Allahabad
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Language Activity
Technical Skills
Backend Development, Frontend Development, Data Architecture, Design Engineering
Hapijs, JavaScript, Algorithms, NodeJS, Ruby on Rails, Web Development, php, Data Structures, c++, Python, mysql, Django, Ember.js
Tools & Libraries
Jenkins (Java), d3js, Neo4j, Apache Thrift (C++), Amazon Kinesis, couchcache
Work Experience
Backend Developer
Saltside Technologies, Bangalore, Karnataka, India
Dec 2016 - Present (1 year and 5 months)
Software Developer
Jun 2015 - Dec 2016 (1 year and 7 months)
Being a part of the core team, I have had to don multiple hats at once here. Have been involved in the architecture and platform forming running the core at Opinio. Have worked on many projects and owned a good number of them from conception to production.The most prominent ones being : Opinio merchant app : Built from ground up and is being used by over 10,000 merchants across India. Owners Dashboard (web) : A cross outlet analytics dashboard for Opinio partner merchants owning multiple business, which gives them a bird's eye view of all of their businesses.Route Graph service : A service used by our Hyper Local team to efficiently find routes and match demand to our riders.CI and Deployment Pipeline : Created a QA managed continuous deployment pipeline for use by the entire tech organization. Technologies : • Creating the Microservices Architecture • Map reduce • MongoDb on Scale • EmberJS - WebApp Development • NodeJS • Neo4J : Graph DB for route graph service • PouchDb & CouchDB - Data sync. • Thrift Large scale web system communication systems • Jenkins for CI setup. • Docker For Deployment
Associate Developer
SAP Labs India
Jun 2014 - Jun 2015 (1 year and 1 months)
During period I have been working on SAP HANA, Lumira and D3.jsBriefly I have been responsible for: 1. Appointed as Dev Lead within just two months of joining & led a team of 5 developers, which included experienced colleagues as well. 2. Multi Dimensional Data Visualizations for Industries 3. Created a Late night cab allocation algorithm for use by Transport team at SAP.
Data team Intern
Wikasta Business and Technical Solutions Pvt. Ltd.
Jan 2014 - May 2014 (5 months)
My role consisted of scraping websites like etc., cleaning organizing the data and storing it. I used both relational database(MySQL) as well as document stores (Mongo DB).Finally I wrote simple Collaborative filtering based recommendation system & analyzed the accuracy on different percentage of the entire dataset and also tested performance metrics of the two databases.
Aug 2011 - Nov 2011 (4 months)
Worked a a server side intern at the Technology vertical of Megarth Sustainable Solutions. Technologies I worked on during the tenure of my intern were :Basic Web development technologies (HTML,CSS, JS, jQuery etc ) Django Framework A part of the bug-ticket and wiki management system.
Socially Augmented Hybrid Recommender System
Jul 2012 - Nov 2012 (5 months)
Applying Machine learning techniques to Minix Process Scheduler July 2013 to December 2013 Members:Shivendra Soni, Anshuman Gautam, Shaloo SinghIn this project we modified the Minix 3 scheduler to allow scheduling with our customized priorities and time slices. The objective of our scheduler is to be as fair as possible so as to minimize the average response time and waiting time of processes.The aim of this project was to design a ‘social network based recommendation system’ for e-commerce websites that gives recommendations influenced by user’s social circle as well (not just based on the user’s purchase history). The system was built with the aim to provide more accurate recommendations than the existing recommendation-engines.Tools and Techniques : PHP, MySQL 4, Data-Mining, Probability and Number Theory
Analysis of Effects of various factors on Workday Loss in Coalmine Industry Incidents
Jan 2013 - Jun 2013 (6 months)
The aim of the project was to develop a relationship between incidents in underground coal mining industry and various factors responsible for it. In order to statistically analyze the data, we used two different techniques: 1) Regression Analysis (Ordinary Multivariate) 2) Neural Networks Analysis ( Simple Back Propagation)Tools and Techniques : MATLAB, Regression Analysis, Artificial Neural Networks
Indian Institute of Information Technology - Allahabad
B.Tech, IT
City Montessori School
2008 - 2009
Won .Net Idea Innovation Challenge
.Net Foundation
Jun, 2012
First Runners Up - What the Hack 2.0Second Runners up among 150 teams who participated, in What the Hack,(hackathon) Built a low-cost smart shelf solution for unobtrusively tracking sales & out of sales situation in a store. Also provided analytics & dashboard out of SAP Lumira & SAP HANA ( Columnar In-memory database).Finalists at Intel IoT Roadshow Hackathon Intel Among the 15 finalists in Intel's IoT roadshow challenge. Built a smart & connected farm solution along with analytics based on parameters like Temperature, soil moisture, air quality etc. It was my first attempt at hardware hacking.Among 50 finalists for ET Power of Ideas Economic TimesSelected to present elevator pitch along with only 50 others, selected nationwide at Economic Times Power of Ideas,2009Won the .Net Idea innovation challenge at IIT Delhi for presenting the most well structured & technically feasible business idea.
Patent : Multi-Dimensional Visual Analytics for Industries
Patent Pending : USPTO (SAP Ref.: 150093)
Associate Developer - SAP Labs India
The patent pertains to capturing data of high dimensionality and converting it to cross table data sets. Finally this data, pertaining to various industries was structured and used to create custom interactive visualizations for SAP Lumira ( Self serviced BI Tools)
Associate Developer@SAP Labs India
Won Go-hack 2017 by Gojek
Jan, 2017
Winners of Go-hack 2017 among 500+ teams that participated.

Link :

We built Rashalytics, a system that promises to mitigate the problem of rash driving by intelligently incentivising or penalising the driver based on his driving style. It has been designed to reduce the number of accidents that have increased with the hyperlocal on-demand delivery requiring breakneck speeds of the various products.

The system is able to extract the data of rich metrics like sharp acceleration, hard braking, sharp turns, etc. from the driver's android phone, which is used to train the machine learning models.
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