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Sukhbir Benipal

Solr Consultant in New York, NY. Elasticsearch Consultant.

Skills: CentOS, Lucene, SOLR, Hadoop HDFS, Yarn, MapReduce, HBase, mySQL, Storm, Spark, Kafka, Redis, MongoDB, Nodejs, Spring, Tomcat, HAProxy, Nginx, Android, iOS, Java, Parse, Firebase, geolocation, Linux, DataCenter, Networking, Elasticsearch, Solr Consultant in New York.

Marketplaces: Product Development for B2B Wholesale Marketplaces and B2C, O2O and C2C Mobile Shopping Marketplaces built around a Social Network and live Messaging with Photo and Video Sharing, Private Group Buying and Selling plus location based Local Users, Groups, Products and Deals Discovery. For India and Global markets. Status – Launched on Play Store.
● WANT - Global B2B shopping
● Benipal - India B2B marketplace
● WANT - B2C Marketplace in India
● want local - O2O shopping marketplace
● beni - C2C shopping marketplace

Logistics: Stealth Mode Product Development for a pan India plus hyper local logistics service to complement “Newco” Shopping Marketplace Shipping and Delivery. ● Created algorithm based optimum routing, Unique 10 digit ID based delivery location, live map delivery status, client initiated re routing, Image recognition based trusted recipient for delivery acceptance and live package plus payment confirmation.

Shopping Search Engine: 300 Million Products. 1.2B Titles and Descriptions. 1B+ Images. 12,000 Merchants.
● Voice Search and Image matching, recognition and search.
● Highly scalable infrastructure with average response times under 100ms.
● Contextual + Relational, neural network based Shopping Search Engine able to understand user queries and provide exact results for “ Blue Bedspread by Martha Stewart from Walmart or Macy's.com or around me for under $500”.
● High Volume Search + Big Data Infrastructure allowing Products and Search Queries to reflect most recent state.
● Built and Managed 40 High Performance Servers in Datacenter with 10G uplinks.

Search Engine Architect / AI Researcher / Supercomputing ● Added partial NLP to search, letting the computer “understand” the query.
● Created a self-healing, self-learning “Auto Product categorization” algorithm that can automatically analyse and categorize products in any of over 30,000 available categories. Successfully used and demonstrated success rate of around 85%.
● Built a 20 Tflops CPU based Super Computer with over 12TB RAM, 640 Cores and 1 PB Storage. Easy to add GPUs to increase total Floating Point computation.
● Theorized and Worked on Computer Vision with small GPU based cluster to provide a better understanding of how neural networks can understand images and “see” Videos.
● Theorized and Researched on Artificial Intelligence using various current open source projects and how integrated usage could provide a better understanding of neural networks and their application to live real world data scenarios by providing computers with the ability to “understand” different datasets, “see” images and videos and roughly match their interconnects.