Sri Khushi Surey

• Constructed and upheld full stack financial applications with Java (Spring Boot) and Angular (TypeScript) facilitating high-volume payment systems managing thousands of transactions daily.
• Developed and executed RESTful microservices for transaction validation and settlement, increasing processing reliability and lowering failure rates by around 30%.
• Constructed dynamic XML generation engine for payment format upgrades (v2/v3 → v9), cutting manual effort by nearly 75% and eliminating frequent formatting errors.
• Created war comparison automation tool decreasing release validation duration from ~60 minutes to 15 minutes
• Orchestrated business-critical payment logic integration, incorporating holiday/weekend settlement awareness, minimizing transaction delays and strengthening SLA compliance.
• Created and optimized PL/SQL data purge scripts, advancing database performance and lowering storage overhead by roughly 20–25%.
• Elevated frontend functionality and user experience via Angular-driven UI improvements, reducing user input mistakes and streamlining workflow efficiency.
• Incorporated SonarQube and Veracode, optimizing code quality and reducing security vulnerabilities in production releases.
• Contributed to agile development cycles through involvement in design discussions, conducting code reviews, and implementing CI/CD pipeline improvements.

Application Traffic Analysis & Anomaly Detection System

• Built an end-to-end machine learning pipeline using Python (Pandas, Scikit-learn, TensorFlow) to analyze historical application traffic data.
• Processed and analyzed large-scale time-series datasets (thousands of daily records) to identify trends and anomalies.
• Performed feature engineering incorporating temporal signals (weekends, holidays, peak hours) to improve model accuracy.
• Implemented clustering and classification models, achieving reliable anomaly detection and improving monitoring coverage in absence of Splunk dashboards.
• Enabled data-driven decision making, helping identify abnormal traffic spikes and optimize system performance.