● Working as a cloud architect and make Cloud integration smoother by helping in high level (HLD) and low level (LLD) architecture review and advisory. Suggest best technology stack.

● Help clients make their digital transformation journey smooth by helping for application transformation, solving their complex issues, new cloud projects, cloud migrations and deployments on various private and public clouds

● Helping clients making their lives easier around software development and maintenance on-prem, hybrid multi-cloud and on public clouds.

● Helping clients adapting to new technology trends, frameworks and tools.

● Conducting and participating in demos, workshops, POCs and conferences around Pivotal Cloud Foundry (PCF) offerings.

● Going extra miles to make our clients successful on Pivotal Cloud Foundry platform for technical and operational feasibility and challenges.

● Learning current cloud technology trends.

Project Name: Worked on multiple projects- EDI, PCC, MSP, MAP Micro-service platforms.

Technologies (Used for multiple projects as horizontal architect):

● EnterpriseJava 8,NodeJS, SpringBoot - DI, MVC RESTful web-services, Batch, GCP (Google Cloud Platfrom), Openshift (Kubernetes), Zuul Gateway, Hystrix, MySQL, Airflow, Liquibase, Tomcat, Maven, Grafana, Prometheus, JProfiler, JMeter, Consul, WireMoc, JUnit 4, Mockito, Maven, Git,Tomcat, etc.

● Developed API automation framework for Microservices using SpringBoot , Java 8, Cucumber JVM, RestAssured, AssertJ, Spring Test, Junit 4.

● Code static analyzer – Sonar (FindBug, PMD)

Worked on multiple projects- EDI, PCC, MSP, MAP Micro-service platforms.

▪ Actively involved in requirement gathering, analysis, design (DLD), data modeling with client and internal customers of Apple.

▪ Worked on the POC with various latest technologies like MongoDB, ElasticSearch and Spring 4.x RESTful web-services to increase performance. It’s a new scratch development project.

▪ Handled and managed around 10 millions of data and implemented search engine which can search in less than 20 ms.

▪ Working in Agile(SCRUM) methodology with good understanding of Atlassian-JIRA tool.

▪ Developing and exposing all search and sync based services using Spring MVC based REST web-services.

▪ Developed API/tool to sync data between Oracle to MongoDB, Oracle to Elasticsearch and MongoDB to Elasticsearch.

▪ Doing performance analysis and profiling using Yourkit profiler and load testing using JMeter.

▪ Involved coding and re-factoring using latest Java, Spring DI, MVC, Integration, JPA and REST WS technologies.

▪ Involved Writing Unit/Integration Test Cases using JUnit and Spring Mock Test framework etc.

▪ Involved defining best coding practices, peer/static code review using FindBug, PMD tools and performance improvement using VisualVM.

Technology:

▪ EnterpriseJava 8,AWS EC2,Spring 4.x - DI, MVC RESTful web-services, Spring Security , Test, MongoDBTemplate, ElasticsearchTemplate, JDBCTemplate, Apache Common pool, Http client, Kafka, MongoDB 4, ElasticSearch, Junit 4, Mockito, SoapUI, Maven, Git, Servers: Tomcat 7, Jetty

Actively involved in requirement gathering, analysis, design (DLD), data modeling with client and internal customers of Apple. Worked on the POC with various latest technologies like MongoDB, ElasticSearch and Spring 4.x RESTful web-services to increase performance. Its a new scratch development project. Handled and managed around 10 millions of data and implemented search engine which can search in less than 20 ms. Working in Agile(SCRUM) methodology with good understanding of Atlassian-JIRA tool. Developing and exposing all search and sync based services using Spring MVC based REST web-services. Developed API/tool to sync data between Oracle to MongoDB, Oracle to Elasticsearch and MongoDB to Elasticsearch. Doing performance analysis and profiling using Yourkit profiler and load testing using JMeter. Involved coding and re-factoring using latest Java, Spring DI, MVC, Integration, JPA and REST

The project aims to create a globally consistent “resource” locator with the Apple “look and feel” to effectively and efficiently point customers to the most appropriate resource location(s) to assist with the problem or situation they are trying to resolve. For example, product sales, break/fix service, training, network installation, etc. The locator will be fed data from a consolidated data source (MongoDB/Big-Data) with the same data elements and data filtering for each region and available across the world.