Jan 2024 – Nov 2024 · 10 mos · Delhi, India · Remote

• Address critical scaling challenges by designing and implementing a cloud-native, event-driven architecture. Transitioned legacy systems into an event-based model, enabling high throughput, resilience, and real-time data processing. This modernization significantly improved system scalability, reduced downtime, and enhanced customer experience, while laying the foundation for future growth.

Aug 2020 – Oct 2022 · 2 yrs 2 mos · Pune/Pimpri-Chinchwad Area

• Joined as the first employee of an $85M funded startup, collaborating directly with the
• founder to design and deliver the proof-of-concept product, which later secured funding
• and was adopted by enterprises including Disney, S&P Global, and Johnson & Johnson.
• Built a microservices-based security and compliance management platform, laying the
• foundation for scalability and enterprise adoption.
• Designed and implemented core backend services, including Authentication, Search,
• Run book, and Parser services, leveraging Spring Security, Spring MVC, Elasticsearch,
• Kafka, Redis Cache, and Amazon RDS on AWS Cloud.
• Architected the backend for scalability, high availability, and low-latency performance,
• enabling smooth onboarding of early customers and meeting investor expectations.
• Established role-based access control (RBAC), engineering best practices, and CI/CD
• pipelines to accelerate product development and ensure compliance with security
• standards control and data-scoping mechanisms to ensure security and compliance.

Jan 2020 – Aug 2020 · 7 mos

May 2019 – Dec 2019 · 7 mos · Gurgaon, India

• Mainly responsible for end to end software development cycle starting from designing, coding, reviewing, JUnit test coverage, code quality, load testing, and deployment of code on production using techology like java, spring, jenkin, docker, Aws cloud etc.

Aug 2016 – Oct 2018 · 2 yrs 2 mos · Gurgaon, India

• Woking On Microservice Architecture with Following Technology.
• Infrastructure - Jenkin, Elastic Container Service, Kafka Streaming Service
• Fabsearch - Fabsearch project handle all search request coming from fabhotels.com site. it responsible for calculating hotel availability, inventory, price and proving other hotel metadata related information like nearby location, etc. it serves thousands of requests per second not only from the direct consumer but also supports third-party searches. the technology stack used for this project is ElasticSearch, Kafka, spring data elastic, etc.
• Fabnotification - Fabnotification microservice responsible for sending all notification which includes email, SMS, and android notifications, all microservice which want to send a notification to consumers publish their data in a Kafka queue. Fabnotification service consumers consume this Kafka topic data and send notification using the desired channel. also, log this data in mongodb with status return by message sending channel. ( Spring Boot, Kafka Confluence, MongoDB, Swagger )
• FabReporting -This microservice responsible for storing all requests send by a user and response serve by other microservice. the business team use to query this data in order to take further business decision. ( Spring Boot , Spring Data Elastic, ElasticSearch, Kafka Confluence, Spring Cloud Zipkin, Swagger )
• FabBooking - ( Spring Boot , Spring Data Mysql , Mysql , Kafka, Spring Cloud Zipkin, Spring Redis Cache , Swagger)
• FabAnalytics - ( Filebeat, Logstash, ElasticSearch, Kibana , Logstash Kafka Plugin)
• FabServerMonitoring - ( PakcetBeat, MatrixBeat, Elasticsearch, Kibana )

May 2015 – Aug 2016 · 1 yr 3 mos · Gurgaon, India

• Working with Cassandra, Achilles, ElasticSearch, Spring security, Spring MVC, Amazon web services, Java, CrateIO, MemSQL, SpaceCurve etc.

Oct 2013 – May 2015 · 1 yr 7 mos · Gurgaon, India

• Worked as java Engineer.

Mar 2012 – Oct 2013 · 1 yr 7 mos · Noida Area, India

• Working as a java developer involve with Keyword Behavior Contextual Network team. Intendment behind the project is to serve the request (for Ads) according to the user’s interest and behavior. (Specific Ads to Specific users matching to their respective interest)