• Led the design and development of the MLBackbone model and its validation process, enhancing ad relevance by 15% through a robust infrastructure for managing the Sesame machine learning model in Amazon SageMaker. Utilized AWS services like Lambda, Step Functions, and SageMaker Studio to address orchestration challenges for daily model execution and monitoring. • Collaborated with the Social Ads Optimization team to maximize ad efficiency, improving targeted ad relevance across platforms like TikTok, Facebook, and Snapchat.• Established alerting mechanisms to monitor model performance and operational health, enabling proactive identification of issues and minimizing downtime.• Managed a ticket queue to address small tasks and optimize ad bidding strategies for social media platforms, improving overall ad efficiency and relevance. management. Leveraged AWS services, including CloudWatch, to track performance metrics and establish alarms for critical thresholds, enhancing operational reliability.• Provided on-call support for high-priority incidents, swiftly resolving issues that impacted ad optimization processes and ensuring minimal downtime. Streamlined the workflow for data validation and feature availability, significantly improving the efficiency of the model deployment process.• Contributed to documentation and best practices for managing ML workflows on SageMaker, promoting knowledge sharing within the team.

• The Project deals with the ecommerce payment transactions. Different modules like order management system, payments services, others. Integration programs for payment gateway, radial payment services, for card authorization, settlements and other validations.• Involved in deployment of the application at various environments (Dev, Testing, Staging, and prod).• Wrote program for integration services using Java, Java POJO, XML, JSON.• Used Maven project management tool for build and deployment of the application.• Used GITLAB as source controls.• Used Docker for deploying SQL and testing.• Writing unit and integration tests with Mockito and Mock framework.• Used log4j for logging.

Successfully completed project titled "TechnoFriend" on J2EE. This project is basically based on three technology such as JAVA, Cloud Computing and Artificial Intelligence.