Mar 2024 – Jan 2025 · 10 mos · Bengaluru, Karnataka, India · Hybrid

• I worked as an Senior engineer at Data Infrastructure team at LinkedIn. Where i am working on building and scaling of data stream pipelines which helps to ingest all LinkedIn's events to data lake storage. Our data pipelines handling trillions of event data on each day.
• FastIngest is a high-performance, scalable data ingestion framework developed by LinkedIn to handle massive streaming data efficiently. It leverages Kafka for data transport and supports batching, partitioning, and parallel processing to achieve low-latency, high-throughput ingestion. Designed for real-time data analytics, it integrates with storage systems like HDFS and Apache Pinot, ensuring fast availability of ingested data. FastIngest uses Apache Helix for task coordination and fault tolerance, enabling automatic failover and dynamic task reassignment. Its asynchronous writes, data compression, and efficient offset management make it suitable for large-scale, real-time event processing in distributed environments.
• https://www.linkedin.com/blog/engineering/open-source/fastingest-low-latency-gobblin

Feb 2021 – Mar 2024 · 3 yrs 1 mo · Chennai, Tamil Nadu, India · Remote

• Workflow State Management System:
• I worked on the workflow platform team, where my responsibility is to design and implement the workflow core library to construct or intrepret json based workflow to graph objects and manage the client’s workflow metadata and state transitions capability.
• Programming Language: Java
• Microservices Development Framework : Spring
• Database : Oracle Sql
• PayPal Disputes Workflow Design and Development:
• I Work on PayPal’s core disputes workflow design and implementation, where my responsibility is to understand the business domain and implement the actual workflow based on our core workflow state management library. I am leading the debit card and Internet banking transactions related disputes workflow development. This system is built based on microservices architecture, which interacts with each other via the Rest client.
• Programming Language: Java (Spring Framework)
• Api integrations: Rest and Grpc integrations
• Message broker : RabbitMq for async state transition
• Database : Oracle Sql, Teradata (For BI and Analytics)

Feb 2019 – Jan 2021 · 1 yr 11 mos · Chennai Area, India

• I was part of Amazon ElasticSearch team, where I am worked in a team where we’re work on Developing Distributed Container based Deployment System for large scale data node clusters minimal or without downtime, Operational pains and ambiguity on making sure the consistency on software’s version across the cluster nodes.
• I worked on Magazine subscriptions management services. I worked on JavaScript, Perl mason template, Spring based workflow systems, lisp based data model systems.
• Areas of Interest : Distributed Data Systems, System Architecture.

Mar 2018 – Feb 2019 · 11 mos · Chennai, Tamil Nadu, India

• Worked as a Ruby on Rails backend developer for ITSM Freshservice product. And also working closely with aws cloud infrastructure and devops operations.
• 1) Infrastructure And Deployment Automation
• Develop a Service which automates all manual works such as database
• migration and deployment works. Where i am responsible for understand the freshworks
• cloud infrastructure and develop an end to end service.
• Language : Ruby.
• Framework : RubyOnRails.
• 2) Worked in designing and manipulating cache(redis) system and background process(sidekiq) system for scaling and optimizing systems.
• 3) Have hands on experience with scalable system design.
• 4) Worked On data pipe line for seamless data import for Freshservice (ITSM) Product.

May 2017 – Mar 2018 · 10 mos

• 1) Sentence Embedding
• Understanding the semantic relationship between two syntactically different
• sentences using various deep learning models. By using this service anyone can find all
• semantically relative sentences or words from a data warehouse.
• Language : Python
• Frameworks : Tensorflow(For Deep learning), Scikit learn(For machine learning)
• 2) Chatter Bot
• To interact with customers to give simulation that they are talking with the support
• agent by using customized decision tree with machine learning sentence classifier.
• Language : Python
• Technologies Used : Flask(For web application development), Rasa core and Rasa nlu for
• machine learning process

Oct 2016 – Apr 2017 · 6 mos