Stack Developer Software

Sai Mahidhar Reddy illuri

***********@*****.*** https://www.linkedin.com/in/mahidhar-illuri/ https://github.com/illuri96 475-***-**** St. Louis, MO

Senior Full Stack Developer with 9+ years of experience architecting distributed systems and cloud-native applications. Demonstrated expertise in building highly scalable microservices using Java/Spring Boot, handling 100K+ daily transactions. Proven track record of implementing high-availability architectures and leading cloud migrations across AWS, Azure, and GCP. Strong background in system design, performance optimization, and distributed computing.

EDUCATION

Masters in Computer Science University of Bridgeport Jan 2018 - May 2019

Bachelors in Electronics and Communications JNTU Hyderabad June 2013 – Mar 2017

PROFESSIONAL EXPERIENCE

Software Developer III Charter Communications Jan 2022 - Present St. Louis, MO

Architected, Designed and developed Unified Inventory Management (Source of Truth for all Products, and Customers Information) microservices eco-system (Java based microservices, Oracle DB, on-prem Linux servers, Jenkins Jobs to manage deployments, Swagger to manage API’s, API Gateway, Eureka, HTML & Bootstrap, JS based console applications)

Architected and led migration of UIM services from on-premises to AWS cloud, implementing GitLab CI/CD pipelines and PostgreSQL for improved scalability

Designed distributed caching solution using Redis cluster, reducing database load by 40% and improving P95 latency from 200ms to 80ms

Implemented centralized Service Activation platform using Spring Cloud components (Eureka, Config Server, API Gateway), reducing service discovery latency

Created organization-wide reusable library for SAML authentication and authorization, adopted by multiple development teams

Orchestrated monolithic to microservices migration, implementing circuit breaker patterns with Resilience4j achieving maximum service availability

Optimized JVM garbage collection and thread pool configurations, reducing memory usage by 30% and API response times by 35%

Led cross-functional teams in implementing CI/CD automation, reducing deployment time by 60% and minimizing production downtime

Introduced observability practices using distributed tracing, improving issue resolution time by 40%.

Drove API versioning and backward compatibility strategies, ensuring seamless integration for existing clients

Led technical design reviews and mentored 3 junior developers in microservices best practices and distributed systems design

Senior Software Engineer Wells Fargo October 2020 - Dec 2021 St. Louis, MO

Designed and Developed a Debit card Pay by phone payment order at the Contact Center Channel to add a new pay from account and submitting a same day ON-US debit card payment on behalf of the customer on a delinquent account with email confirmation sent to owner. This processing system is built using Pivotal Cloud Foundry (PCF) and Cardholder Data Environment and implementing Angular for GUI and Microservices architecture using Gradle. This system is designed and has been planned as a cross-organization unified framework for payments.

Developed secure payment processing system using Spring Boot microservices and Angular, handling peak loads of 10000 TPS

Implemented event-driven architecture using Kafka for real-time transaction processing with exactly once delivery semantics

Designed scalable architecture supporting 5K+ concurrent users with maximum availability in PCI-compliant environment

Created comprehensive API documentation using Open API/Swagger, increasing developer onboarding efficiency

Implemented a self-healing mechanism using Kubernetes and Prometheus, reducing incident response times by 50%.

Orchestrated DevSecOps initiatives, ensuring secure coding practices and compliance with financial regulations.

Leveraged Axon Framework to rebuild payment processing modules using event sourcing, ensuring atomic transaction consistency and reducing recovery time during failures by 40%.

Drove API gateway enhancements, implementing rate limiting and authentication strategies for improved security and scalability.

Maintained 95% test coverage using JUnit and Mockito, adhering to SonarQube quality gates

Software Engineer Vanguard June 2019 - September 2020 Malvern, PA

Fixed Income Group of Vanguard is in the modernization journey to upgrade the Basket Analytics Platform in AWS. As part of these efforts the legacy applications in the GemFire platform were broken down into microservices, step functions, batch applications and an asynchronous model using SQS/SNS. The data storage used is RDS and S3 and Continuous integration setup using Bamboo.

Modernized Basket Analytics Platform using AWS serverless architecture (Lambda, Step Functions, SQS/SNS)

Migrated legacy Basket Analytics creation using Spring Boot batch that processes data from the upstream processes and creates the S3 Analytics file

Designed and Developed Message Consumers (SQS, SNS, Kinesis) that triggers the batch applications depending on the Payload to process different types of Funds and Baskets

Designed and Developed REST Api consumers to process data from multiple Apis Optimized expensive operations using the appropriate usage of Caching and Asynchronous executors

Involved in troubleshooting application issues using Splunk and CloudWatch and developed solutions

after approvals from the Architecture team

Implemented circuit breaker and bulkhead patterns for improved system resilience

Designed data partitioning strategy for large-scale financial data processing across multiple fund categories

Optimized batch processing performance through caching and async executors, reducing processing time by 40%

Developed a comprehensive data archival solution using AWS Glacier, ensuring cost-effective storage management for historical financial records.

Designed a dynamic schema validation system, allowing for flexible data ingestion from multiple financial sources.

Collaborated with business stakeholders to refine reporting dashboards, enabling real-time financial analytics for fund managers.

Software Developer Reliance Retail June 2016 - December 2018 Bangalore, India

●Developed Restful web-services for the product and customer data for the retail - marketplace platforms for other services to consume in Spring Boot

●Developed secure authentication mechanisms, including OAuth2, JWT, and role-based access control (RBAC), to ensure robust user data protection.

●DynamoDB was the database used and S3 was the object storage used

●Used AngularJS to create views in the front-end

●Implemented asynchronous messaging services using Kafka for handling customer events

●Implemented and managed automated CI/CD pipelines with Jenkins, AWS Code Pipeline

●Created and executed unit tests using JUnit4 and Mockito for integration tests

●Elasticsearch, Logstash and Kibana for logging

SKILLS

●Core Technologies: Java 8/11/17, Spring Framework, Spring Boot, Microservices, REST APIs, JavaScript, TypeScript

●Frontend: React.js, Angular, HTML5, CSS3, Bootstrap, Vue.js

●Databases & Caching: Oracle, PostgreSQL, MySQL, MongoDB, Redis, SQL optimization, Connection Pooling

●Cloud & DevOps: AWS (S3, RDS, SQS, SNS, Lambda), Docker, Kubernetes, Jenkins, GitLab CI/CD, PCF, Azure, GCP

●Message Queuing: Apache Kafka, RabbitMQ, AWS Kinesis

●Testing & Quality: JUnit, JMeter, Mockito, Selenium, Postman, Karma, Cypress, Jasmine, Jest

●Monitoring & Logging: Splunk, ELK Stack (Elasticsearch, Logstash, Kibana), Prometheus, Grafana

●Security: SAML, OAuth2, JWT

●API Management: API Gateway (AWS), Apigee, Swagger

●Data Serialization: JSON, XML, YAML

●Project Management Tools: Jira, Asana, Trello

●Configuration Management: Ansible, Chef

PROJECTS & ACHEIVEMENTS

System Design: Event-Sourced Architectures, Designed and documented distributed payment processing system architecture for Wells Fargo org, Design and Implement Internet Plan of Record for Charter org.

Teaching Assistant: Conducted labs in Deep Learning and Neural Networks

Relevant Coursework: Distributed Systems, Advanced Algorithms, Cloud Computing, Deep Learning

Research: Neural Network Optimization for Self-Driving Cars, focusing on distributed consensus algorithms

Current Progress: Core LLM Concepts, Accessing LLM APIs to Integrate into Web Apps, Multi-modal AI

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