Project Name - Compass/CSRTClient : Amex. Jul’15 to Till DateDesignation/Role: SSE.Platform & Skills:Hadoop, Hive, Java (back-end), Rest Easy, Tomcat, Hbase (M7), Spring Boot, Redis, APIGEE, Activiti, Quartz APIBrief: One platform for all. Merchant / Offer recommendation use cases hosted in one platform. Data as Service – merchant, customer, transaction . . . data available as a service.Data Governance – platform owner should be able to configure what data can be used by which use case.Plug and Play – extension points to build very focused and targeted use case.Monitoring – centralized and real time monitoring of system health, SLA.My key responsibilities are:• Design and development• Analyzing the given problem statement• Participating in design discussions and analyze the problem in detail to understand the key points

Collaborative filtering is commonly used for recommender systems. These techniques aim to fill in the missing entries of a user-item association matrix.In this project we have write implantation for one of the collaborative filtering algorithm called PPMF (probabilistic matrix factorization), which is much more robust then ALS whose implementation already there at spark MLlib.Technologies used:- language used Scala with framework Spark batch, also Spark Streaming with Kafka to get the input to the model. This solution is now integrated with StreamAnalytix (product of impetus)

Application enablement.Application Enablement is an industry vision and network approach that combines the trusted capabilities of network operators and the speed and innovation of the web to provide end users and enterprises what they demand: a richer and more trusted web experience and beyond.Experience in handling various java and php projects within a team. Business domains vary from e-commerce, telecom, network management, Reporting and Analytics.