Java Full Stack Developer - AI-Driven Microservices Expert

Swagath Reddy Malkannagari

Java Full Stack Developer

860-***-**** **********@*****.*** United States (Open to relocate) PROFESSIONAL SUMMARY

Results-driven Java Full Stack Developer with 4+ years of experience building AI-enabled, cloud-native, and large-scale enterprise systems. Skilled in ML-driven microservices, high-performance Spring Boot APIs, and intelligent Angular/React frontends used by millions. Strong expertise in AWS- optimized distributed architectures, Kafka streaming, predictive analytics, and end-to-end CI/CD automation. Proven ability to boost system performance by up to 40%, cut cloud costs by 20–30%, and deliver mission-critical solutions for real-time processing, fraud detection, and large-scale data platforms.

SKILLS

• Languages: Java, Python (AI/ML), SQL, JavaScript (ES6+), TypeScript

• Backend: Spring Boot, Spring MVC, Spring WebFlux, Spring Security, Microservices, Hibernate, JPA

• Frontend: Angular, React.js, Redux, RxJS, HTML5, CSS3, Bootstrap

• AI / Machine Learning: Scikit-learn, TensorFlow (basic), ML APIs, Feature Engineering, Model Deployment, Data Pipelines

• Cloud & DevOps: AWS (Lambda, EC2, EKS, ECS, API Gateway, S3, SQS), Docker, Kubernetes, Jenkins, GitHub Actions, GitLab CI/CD, Terraform

• Databases: Oracle, PostgreSQL, MongoDB, DynamoDB, SQL Server

• Messaging & Streaming: Apache Kafka, RabbitMQ, JMS

• Monitoring & Observability: Prometheus, Grafana, ELK Stack, CloudWatch

• Methodologies & Architecture: Agile/Scrum, TDD, CI/CD, Event-Driven Architecture, Clean Architecture, Distributed Systems WORK EXPERIENCE

JPMorgan Chase& Co. Sep 2024 – Present NH

Java Full Stack Developer

• Engineered AI-enhanced Java/Spring Boot microservices integrated with Python-based ML models to analyze 50M+ monthly transactions, increasing fraud-detection accuracy by 35% and reducing false positives by 20%, delivering multi-million-dollar annual risk-mitigation value.

• Designed and implemented predictive caching & intelligent pre-fetching in React.js for real-time trading dashboards accessed by 10M+ global users, reducing load times by 28% and enhancing system responsiveness during peak market hours.

• Modernized core legacy systems into a machine-learning-optimized distributed architecture using REST + GraphQL, increasing data processing throughput by 32% across pipelines handling billions of data points daily.

• Deployed ML-enabled microservices on AWS EKS with autoscaling, maintaining 99.99% uptime and supporting an 18% increase in transaction volume

(from 250M to 295M monthly operations) without service degradation.

• Upgraded CI/CD pipelines with automated ML model versioning, testing, and real-time inference deployments, reducing release cycle time by 25% and ensuring seamless rollout of high-impact production models.

• Achieved a 30% reduction in AWS compute costs (saving over $500K annually) by implementing rightsizing, spot instance utilization, and predictive autoscaling for compute-intensive ML workloads.

Capgemini May 2022 – Jul 2023 India

Java Full Stack Developer - II

• Integrated ML-based classification and recommendation pipelines into Spring Boot APIs, enabling data-driven decisioning across 12+ enterprise workflows and improving prediction accuracy by 40%, impacting 3M+ monthly transactions.

• Enhanced RxJS-powered asynchronous flows with predictive modeling, reducing UI latency by 25% and delivering faster real-time updates for 1.5M+ active users across high-traffic applications.

• Engineered cloud-native microservices for AI-driven analytics on AWS, increasing overall platform performance by 30% and reducing cloud expenditure by 22%, saving over $1.2M annually in infrastructure costs.

• Optimized JPA/Hibernate data access layers with intelligent distributed caching, cutting query latency by 33% across databases processing more than 500M records, significantly boosting throughput and SLA compliance.

• Implemented GraphQL data pipelines with MongoDB to support scalable, ML-ready architectures handling billions of data points, enabling faster model inference and seamless frontend-backend integration.

• Automated full-stack CI/CD workflows using AWS CodePipeline, accelerating deployment cycles by 27%, supporting 1,000+ builds annually, and ensuring zero-downtime releases for mission-critical systems. Fusion Software Technologies Mar 2021 – Apr 2022 India Java Full Stack Developer – I

• Engineered intelligent Angular UI components powered by ML-based personalization models, driving a 20% boost in user engagement across 1.5M+ monthly active users.

• Developed and optimized high-performance Spring Boot APIs that reduced response times for mission-critical workflows by 18%, accelerating processing for 10M+ daily transactions.

• Enhanced code reliability by 30% through rigorous TDD and automation frameworks, resulting in a 15% reduction in production defects across systems handling multi-billion-dollar business operations.

• Designed and deployed real-time data ingestion pipelines leveraging Kafka & MongoDB, enabling low-latency processing of 50M+ events per day and powering organization-wide analytics dashboards.

• Architected robust DAO and service layers using Hibernate & JDBC templates, ensuring seamless and scalable data flows across enterprise systems serving over 100M+ records.

• Strengthened cross-functional engineering by delivering detailed Postman API documentation and standardized test suites, accelerating development collaboration across global teams of 200+ engineers. EDUCATION

Masters in Computer Science Rivier University, Nashua, NH, USA

Contact this candidate