• Data modernisation from in house legacy DB2 to AWS Aurora DB using PySpark, AWS Glue workflows orchestrated by AWS EventBridge, AWS Step Functions and multiple other services for one time migration and batch data processing for the second largest investment firm in the world.• Integrated 13 legacy mainframe files into 6 integration and 4 final producer jobs(containing over 1 billion+ records to AWS Aurora), executing over 80 business rules. while facilitating a ~50% reduction in code complexity and achieving ~90% reduction in daily batch expenses for the client.• Owned and managed issues of 2 of the 4 end tables along with analysis and data consistency checks throughout the data migration process implementing mismatch fixes(from 80000 to 0) between legacy and cloud database for daily batch runs.• Coordinated the deployment of over 10 AWS services (including Glue, EC2, Lambda, EventBridge, Workflow, Step functions, etc.) along with BMC Control-M to create and manage scheduling tasks. This effort streamlined data flow and enhanced efficiency by more than 4 times compared to the legacy pipeline.• Worked on a Gen AI project for a top e-commerce website as a prompt engineer, came up with prompts to enhance model performance. Analyzed data and feedback to continually improve prompt generation strategies.

• Associated with the testing and evaluation of Advanced Telemetry Systems package - QDTE Lab. Developed an optimised approach to test individual packages against variable temperature and pressure conditions by simulation and analyzed using MATLab.• Built a custom python function using the 'ctype' library to get C execution speeds on top of the existing function which was used in earlier analysis stages.