At PayPal, I lead the development of Java Spring Boot microservices for high-volume payment systems, reducing API response times by 35% while ensuring PCI-DSS compliance. I built Kafka-based pipelines for real-time fraud detection and transaction validation, improving accuracy by 30% and safeguarding $500M+ in annual transactions. I automated deployments with Jenkins, Terraform, and AWS EKS, cutting release cycles by 40% and scaling services to support millions of daily transactions with 99.99% uptime. By enhancing monitoring and observability with Prometheus and Grafana, I reduced MTTR by 25%, enabling faster recovery and greater system resilience.

As a Software Engineer at General Motors, I contributed to the design and development of large-scale backend systems that powered connected vehicle platforms. I built Java Spring Boot microservices to process over 1M daily telemetry points, and designed Kafka + AWS Lambda/Kinesis pipelines for real-time data ingestion and transformation. I also integrated predictive maintenance models (Python, Scikit-learn) into backend workflows to reduce vehicle downtime and improve reliability. My work included developing secure Hibernate + PostgreSQL APIs for telematics data, deploying services to Kubernetes, and implementing monitoring with Prometheus & Grafana. These efforts enabled real-time analytics dashboards that improved decision-making for vehicle performance and reduced fleet maintenance costs by 15%.

At Dr. Reddy’s, I developed Java-based ETL pipelines and Spring Boot APIs to process and secure large volumes of pharmaceutical and clinical trial data. I automated data workflows with AWS Glue and Lambda, reducing refresh cycles from 24 hours to under 2 hours. I also integrated machine learning models in Python to predict patient adherence and drug effectiveness, accelerating insights for clinical teams. Additionally, I designed analytics dashboards with Java services and Power BI for real-time trial monitoring and compliance tracking. These initiatives improved data processing efficiency by 50% and shortened FDA submission timelines by 30%, supporting faster and more accurate regulatory reporting.