You found the secret! Pranav has been writing Java since
2016 โ
that's 8+ years of turning
โ into production-ready microservices.
โโโโโโโโBA โ old habits die hard ๐
4 years building cloud-native Java systems for Healthcare & Insurance in the US. Currently at McKesson โ shipping microservices that process 10,000+ daily transactions.
Hello! I'm Pranav Reddy Pajjuru, a Full Stack Java Developer with 4 years of experience building cloud-native, microservices-based enterprise applications in Healthcare and Insurance domains across the United States and India.
Currently at McKesson (Jan 2024โPresent), I build pharmacist dashboards in React/Redux, Spring Boot microservices, and Kafka-driven prescription pipelines processing 10,000+ daily transactions with zero message loss. Previously at Progressive Insurance, I overhauled 100+ Spring Boot REST & SOAP services supporting $2M+ in annual digital policy revenue.
My stack is deep on the JVM side โ Java 8/11/17/21, Spring ecosystem, Kafka โ paired with solid React frontend skills and hands-on AWS (EKS, EC2, S3, RDS, DynamoDB) deployments using Docker, Kubernetes, and Terraform.
Technologies I use daily to design, build, and deploy production systems.
React/Redux pharmacist dashboards with 2D barcode validation processing 10,000+ daily prescriptions. Kafka event pipelines with zero message loss, Redis caching saving ~$15K/year, and HIPAA-compliant OAuth2 security across 20+ APIs.
100+ Spring Boot REST & SOAP services for digital insurance checkout supporting $2M+ annual revenue. Improved transaction success rates by 15% and cut integration failures by 20% across Auto, Fire, Life, and Health products.
Monolithic Java 8 web app with Servlets, JSP, JDBC for internal workforce and project management. Reduced manual reporting effort by 35% with Oracle DB-backed attendance and expense tracking modules.
NLP-powered pipeline to analyze customer feedback from surveys, social media, and reviews โ identifying trends and sentiment. Used TensorFlow for sentiment classification and AWS Glue for scalable ingestion. Amazon QuickSight dashboards drove 15 critical product enhancements.
Real-time chat app using Node.js and AWS Lambda improving user communication by 20%. AWS API Gateway WebSocket enabled Socket-based bidirectional communication reducing latency for 10,000+ concurrent users, deployed on Kubernetes with auto-scaling.
Distributed web app for real-time traffic monitoring using Java, Spring Boot, and Kafka. Boosted analytics performance by 30% using AWS DynamoDB with Apache Spark. RESTful APIs enabled seamless integration with external traffic monitoring systems.
Open to full-time roles, freelance, and interesting collaborations. I reply within 24 hours.