R

Rahul Podugu

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Maryland, United States

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work
Job

Résumé

Jobs verified_user 0% verified
  • A
    Software EngineerII
    Aztra Ai
    Jan 2024 - Current (2 years 6 months)
    • Designed and implemented a high-throughput payment orchestration engine using Java, Spring Boot, and Kafka Streams; processed 2M+ daily transactions with 99.9% uptime, reduced failures by 78%, and increased throughput by 45%. • Engineered a horizontally scalable inventory microservice on AWS EKS using Spring Boot, Docker, and DynamoDB; introduced asyn chronous I/O and connection pooling to reduce P99 API latency by 65% and sustain 3× traffic during flash sale events. • Led mentoring of 4junior developers via biweekly code reviews, pair programming sessions, and Sprint retrospectives; increased story point throughput by 30% and reduced onboarding time by 40%. • Deployed end-to-end observability using Prometheus, Grafana, and CloudWatch
  • U
    Software Engineer,ResearchAssistant
    University of Maryland Baltimore County (UMBC)
    Sep 2022 - Nov 2023 (1 year 3 months)
    • Revamped a high-frequency data visualization dashboard using Angular Universal (SSR), HTML5, and CSS3; implemented lazy loading and asset optimization to improve Time to Interactive (TTI) by 40%. • Developed containerized Node.js microservices exposing REST and GraphQL endpoints for research datasets (10GB+); deployed on Kuber netes with horizontal pod autoscaling to handle concurrent access spikes. • Integrated Redis as a caching layer for frequently accessed APIs, improving average response time by 35%; implemented stateless JWT authentication with role-based access control for over 10K users.
  • UST
    SoftwareDevelopment Engineer
    UST
    Aug 2021 - Jun 2022 (11 months)
    • Designed and maintained modular Spring Boot services with Hibernate ORM to support catalog, cart, and checkout workflows for a high traffic e-commerce platform (¿100K DAUs). • Built and secured payment APIs integrating Stripe and PayPal SDKs with retry handling and webhook validation; increased successful transaction rate by 15%. • Architected an asynchronous data sync layer using AWS SQS and SNS to handle eventual consistency across order, billing, and inventory domains, improving cross-service data accuracy by 85%. • Worked in a cross-functional 6-member Agile team using Jira and daily standups; improved velocity by implementing burndown tracking and DOR/DoD definitions. • Used VisualVM and Heap Dumps to profile JVM memory leaks an
  • Fidelity National Financial
    Software EngineerIntern
    Fidelity National Financial
    Apr 2021 - Aug 2021 (5 months)
    • Resolved 200+ Java/Spring and SQL mapping inconsistencies during legacy-to-cloud migration, improving ETL data integrity by 50% and reducing migration failures by 25%. • Developed a Java utility with JDBC and Apache POI to automate batch data pulls from legacy systems; accelerated extraction by 30% and improved downstream reporting accuracy. • Refactored memory-heavy backend services and optimized Hibernate HQLjoins and indexes, reducing JVM crashes by 40% and increasing response speed by 25%. • Built CI/CD pipelines with Jenkinsfiles and Kubernetes manifests; enabled rolling updates with health checks and reduced downtime to zero while cutting deployment time by 20%.
Education verified_user 0% verified
  • Amrita Vishwa Vidyapeetham
    B.Tech in Computer
    Amrita Vishwa Vidyapeetham
  • University of Maryland
    M.S. in
    University of Maryland
Projects (professional or personal) verified_user 0% verified
  • D
    Decentralized Secure File Sharing
    • Designed a peer-to-peer encrypted file sharing system using RSA/AES and a distributed hash table. • Achieved zero data breaches in 12 months; 3× async I/O.
  • R
    Real-time Stock Trading Engine GitHub
    • Built lock-free, concurrent stock trading engine using Java and atomic operations. • Delivered millisecond-level trade matching across 1000+ tickers.
  • D
    Distributed Cache System
    • Built a Redis-based distributed cache with consistent hashing and sharding; supported 10K+ RPS with sub-ms latency. • Deployed CloudWatch alerts for real-time issue detection; ensured 99.9% system availability.