Senior Software Engineer Cloud-Native Microservices Expert

Vamsi Krishna Koppineedi

********@***********.*** +1-813-***-**** LinkedIn USA (Open to Relocate) SUMMARY

Results-driven Software Engineer with 4+ years of experience designing and delivering cloud-native microservices, distributed systems, and high-availability platforms across financial and enterprise domains. Proven expertise in Java, Python, Spring Boot, AWS, Azure, Kubernetes, and AI/ML integration, supporting 5M+ monthly transactions, 100K+ users, and systems with 99.99% uptime while driving

$500K+ cost savings through automation and optimization. Strong background in API development, event-driven architecture, CI/CD, and performance engineering, with demonstrated success improving scalability, reliability, and deployment velocity in Agile environments. Recognized for building AI-powered solutions and enterprise DevOps pipelines that enhance operational efficiency and accelerate business outcomes.

SKILLS

• Programming Languages: Java, Python, C++, JavaScript, SQL, TypeScript, HTML5, CSS3

• Frameworks & Libraries: Spring Boot, Spring Security, Hibernate/JPA, FastAPI, Flask, Node.js, React.js, Angular, REST APIs

• Cloud & DevOps: AWS (EC2, S3, RDS, Lambda, ECS, SQS, Aurora, CloudWatch), Microsoft Azure, Docker, Kubernetes, Jenkins, CI/CD Pipelines, Terraform (Infrastructure as Code)

• Databases: PostgreSQL, MySQL, MongoDB, DynamoDB, Redis, Data Modeling, Query Optimization

• Architecture: Microservices, Event-Driven Architecture, Distributed Systems, High-Availability Systems, Multithreading, Concurrency, Performance Optimization

• Testing & Quality: PyTest, Unit Testing, Test Automation, Regression Testing, TDD, Agile/Scrum, CI/CD

• Security & Compliance: OAuth2, JWT Authentication, Secure API Development

• AI / ML: Generative AI, LLM Integration, OpenAI APIs, LangChain, PyTorch, Predictive Analytics, Anomaly Detection

• Operating Systems & Tools: Linux (Ubuntu, RHEL), Git, JIRA, Confluence, Kubernetes

• Software Engineering Practices: System Design, Secure Coding, Technical Documentation, SDLC WORK EXPERIENCE

Goldman Sachs, FL May 2024 – Current

Software Engineer

• Architected and delivered a cloud-native microservices platform using Java, Spring Boot, AWS, and Kubernetes, processing 5M+ transactions/month with 99.99% availability and enterprise-grade resiliency.

• Designed and deployed 12+ scalable REST APIs supporting 100K+ daily users, reducing latency by 40% through caching, asynchronous processing, and performance optimization.

• Engineered an event-driven architecture with AWS SQS and distributed messaging, improving peak scalability by 3 and enhancing reliability for high-volume financial systems.

• Optimized cloud infrastructure utilization across compute and storage, delivering $500K+ annual savings (30%) through automation, rightsizing, and governance controls.

• Led development of AI-driven fraud detection and risk analytics pipelines using Python and ML, improving detection accuracy by 28% and reducing manual investigation effort by 70%.

• Implemented enterprise CI/CD pipelines with Jenkins, Docker, and Kubernetes, reducing release cycles from 2 weeks to 48 hours, achieving 99% deployment success, and building an LLM-powered engineering assistant that saved 1,000+ hours annually. LTI Mindtree, India April 2021 – July 2023

Full Stack Software Engineer

• Engineered and deployed 20+ production-grade RESTful APIs using Python (FastAPI/Flask) and Microsoft Azure, supporting mission- critical workloads with 99.9% uptime SLA, high availability, and scalable cloud architecture.

• Led modernization of legacy monolithic systems into microservices architecture, improving platform scalability by 60%, reducing production incidents by 35%, and accelerating feature delivery timelines.

• Developed high-performance React-based web applications with reusable components and optimized UI workflows, increasing user productivity by 30% and reducing customer support tickets by 25%.

• Implemented Redis caching and database performance optimization strategies, reducing database load by 80% and improving application response latency by 25%, enabling near real-time analytics processing.

• Automated cloud infrastructure provisioning and CI/CD pipelines using Azure DevOps and Infrastructure-as-Code, processing 2.5M+ events/month, reducing release cycles by 50%, and saving 11+ engineering hours weekly.

• Built AI-driven anomaly detection and predictive monitoring solutions using Python and machine learning, improving system monitoring accuracy by 30%, increasing regression test coverage to 90%, and reducing production defects by 40%. EDUCATION

Master of Science in Computer Science University of South Florida, Tampa, Florida Bachelors Computer Science and Engineering SRKR Engineering College, Andhra Pradesh, India

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