Senior Java Backend Engineer Cloud-Native Microservices Expert

Adi Pratyush

+1-669-***-**** ************@*****.*** California, USA (Open to Relocation)

Professional Summary

Software Engineer with 5+ years of experience in backend and cloud-native development specializing in Java 17, Spring Boot, Microservices, AWS (EKS, Aurora, S3), Kubernetes, Kafka, and Generative AI. Delivered a destination ranking service processing 1.8M travel interactions and telecom billing microservices handling 200K+ daily transactions. Built high-availability systems sustaining 4x traffic spikes at 99.9% uptime, reducing latency by 57% and CPU utilization by 20%. Strong foundation in DSA, system design, event-driven architecture, and API governance. Technical Skills

Languages & Frameworks: Java 17, Spring Boot 3, Hibernate/JPA, Python, Flask, FastAPI, Node.js, Express.js, JavaScript, TypeScript, React.js, Next.js, GraphQL, RESTful APIs Cloud & DevOps: AWS (EKS, EC2, S3, Lambda, API Gateway, Aurora PostgreSQL, CloudFront, Route 53), Terraform, Docker, Kubernetes, HPA, CI/CD (Jenkins, Helm), Blue-Green & Rolling Deployments Databases & Caching: PostgreSQL, MySQL, MongoDB, DynamoDB, Redis, Memcached, ElasticSearch, OpenSearch, Indexing, Sharding, Query Optimization

Messaging & Streaming: Apache Kafka, Event-Driven Architecture, Idempotent Consumers, Dead-Letter Queues, Retry Patterns AI & Embeddings: Generative AI, Vector Similarity Search, BERT, Embedding Pipelines, OpenSearch, Semantic Recommendations Observability & Security: Prometheus, Grafana, OpenTelemetry, Datadog, CloudWatch, Splunk, OAuth2, JWT, RBAC, OpenAPI/Swagger

Testing & Agile: JUnit, Mockito, Jest, Cypress, Selenium, SonarQube, Postman/Newman, Scrum/Kanban, JIRA, Code Reviews Professional Experience

American Express January 2025 – Present

Software Development Engineer California, USA (Hybrid)

– Designed a Java 17 destination ranking service using Spring Boot 3, processing 1.8M travel interactions across 14 attributes with heap-based top-K ranking and time-decay scoring, reducing latency from 2s to under 800ms (P95 < 150ms).

– Built a personalized discovery API with ElasticSearch geo-spatial indexing and query profiling, cutting median search latency from 280ms to 120ms and improving relevance via controlled A/B testing.

– Configured Redis distributed caching (cache-aside, TTL tuning, request coalescing) to prevent cache stampede, reducing Aurora read load by 35% and achieving near real-time content refresh.

– Architected services on AWS EKS with Aurora PostgreSQL multi-AZ, S3, and CloudFront; configured HPA and blue-green deployments to sustain 4x campaign traffic at 99.9% availability.

– Optimized JVM performance via G1GC tuning and non-blocking I/O (CompletableFuture), reducing CPU utilization by 20% under 12K RPS load through heap and thread dump analysis.

– Integrated an embedding-based GenAI recommendation module using vector similarity search on OpenSearch with batched embedding pipelines under 300ms latency budget and fallback handling for AI degradation. Amdocs June 2019 – June 2023

Software Engineer India

– Engineered Spring Boot telecom billing microservices for subscription lifecycle and invoicing, processing 100K–200K daily transactions; optimized SQL queries across 18+ tables via indexing and execution plan analysis.

– Built Kafka event-driven workflows across 11+ topics with idempotent consumers, retry logic, and dead-letter handling, ensuring reliable billing event processing at enterprise scale.

– Secured 17+ REST APIs with Spring Security, OAuth2, and JWT; enforced RBAC and resolved audit findings through improved token validation and endpoint hardening.

– Deployed 10+ containerized microservices on Kubernetes EKS with autoscaling and health probes, sustaining 2x traffic spikes during month-end billing cycles without degradation.

– Automated AWS infrastructure via Terraform (VPCs, IAM, EKS, RDS), reducing environment setup time by 30%; hardened CI/CD in Jenkins with SonarQube and JUnit quality gates across critical billing modules. Academic Projects

Real-Time E-Commerce Recommendation Engine Java 17, Kafka, Redis, AWS EKS, Kubernetes, Prometheus, Grafana

– Built an event-driven recommendation system with Kafka reactive streams, Redis caching, and versioned REST APIs on AWS EKS with OAuth2, handling 50K recommendation requests/minute with full monitoring. Real-Time Customer Sentiment Platform Python, Flask, Docker, BERT

– Containerized full-stack sentiment analysis platform using Flask and BERT delivering sub-100ms inference latency with a real-time interactive dashboard.

Education

University of Colorado Denver August 2023 – May 2025 Master of Science, Computer Science — Graduate Teaching Assistant Denver, CO, USA Birla Institute of Technology, Mesra August 2015 – May 2019 Bachelor of Engineering, Computer Science India