Technical leader in Compliance Transaction org of PayPal.

Leading various areas within Compliance: screening, case investigations, mass simulation platform for Compliance Sanctions and Transaction Lifecycle for Transaction management.

Designing and developing scalable solutions for real-time transaction screening which includes microservices, messaging queues, fail-safe batches, etc.

Contributed to geo localization projects for setting up compliance screening services.

Senior engineer for Compliance Fircosoft Integration project in PayPal.

Containerized various third party executables using docker and made it comptabile for PayPal production

standards within microservices and spring batches.

Introduced and developed Compliance sanctions first mass simulation environment where containized

applications can be deployed.

Brought down the sanctions rule tuning exercise for Compliance business team from multiple days to few hours with simulation platform capable of scanning 300 million transactions under 2 hours.

Leading various sub-domains within Compliance sanctions: case-bridge, rule tuning and adjudication pipeline.

currently working with the Enterprise Risk and Compliance team of PayPal.

Developing microservices which are used to scan the transactions happening on the PayPal platform against fraudulent activities, rules, and regulations of the government.

Building RESTful services which will be used in the integration of a new engine "Fircosoft" for scanning the transaction.

Dockerization of micro-services to make it work standalone and ready to deploy state.

Technologies used are Java 8, Spring boot, REST, Docker.

• Design and Implement big data algorithm for researchers, integrating them into the IP2 pipeline and

making a web-based user interface. These algorithms require intensive search and computational logic.

• Maintaining the file storage over the server and server-side debugging on Linux based systems.

• Keeping track of scheduler over the cluster so that computation jobs submitted are properly returned.

• Configured jobs to run on parallel using HPC systems.

• Designing and implementing algorithms which can handle big proteomics data and integrate them into

the pipeline.

• Working on and improving the current analysis tool in the pipeline for better throughput of results.

• Installation of SGE cluster over client's platform which is used to schedule computation jobs submitted

by the user.

• Handling over five customer servers for any troubleshooting or server-side debugging.

• Backing up and syncing data from work server to backup server on a regular basis using efficient open

source utility like ‘rsync’ on Linux platform.