|
Current Focus
Spring Boot
Microservices & REST APIs
const dev = {
name: "Ammannaidu",
role: "Backend + AI Engineer",
stack: "Java · Python · LangChain"
};
Ammannaidu
Gollapalli
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Tech Focus
Spring Boot
Microservices & REST APIs
const developer = {
name: "Ammannaidu",
role: "Senior Software Engineer",
location: "Visakhapatnam, IN",
focus: "Backend systems + Agentic AI"
};
Ready to create something amazing?
About Me
Backend engineer by training, AI engineer by direction — I build the unglamorous parts that keep systems (and now, agents) running
Four Years in the Machine Room
I started out where most of the internet's boring-but-critical work happens: enterprise backend systems nobody sees until they break. At Infosys, I spent four years as the engineer companies like BNSF Railways and Mercedes-Benz called in to modernize what legacy code had left behind — pulling 25+ mainframe modules into 40+ independently deployable microservices, one careful migration at a time.
Owned end-to-end migration of 25+ legacy modules into 40+ production REST APIs
Cut p99 latency 20% on services handling 5M+ daily requests
Shipped 99.9%-uptime services for a Fortune 500 automotive OEM
Sat as the direct client point of contact, not just a ticket-taker
Professional Experience
Building scalable applications and delivering exceptional user experiences across diverse industries
Senior Systems Engineer
Full-timeKey Responsibilities & Achievements:
- Engineered migration of 25+ legacy mainframe modules to Java-based microservices, improving scalability and reducing maintenance overhead by 30%
- Architected and developed 40+ REST APIs using Spring Boot, JPA, Hibernate, and optimized MySQL queries, resulting in 20-25% faster response times
- Performed in-depth analysis of legacy workflows with SMEs and defined end-to-end migration strategies, reducing technical risks and rework by 15%
- Collaborated cross-functionally with architects, QA, and product teams to maintain code quality and achieve 95% on-time delivery for major releases
- Led code reviews and enforced SOLID principles, improving code consistency and maintainability; implemented JUnit test cases achieving 80%+ coverage
Technologies Used:
System Engineer
Full-timeKey Responsibilities & Achievements:
- Developed high-availability backend services using Java, Spring Boot, and MySQL, improving system uptime to 99.9%
- Designed and implemented RESTful APIs for dealer information retrieval, enabling seamless integration with React front-end applications
- Automated daily ETL workflows and data ingestion using scheduled jobs, reducing manual effort by 90% and improving data reliability
- Worked in Agile Scrum teams, contributing to sprint planning, peer reviews, and achieving consistent sprint velocity
- Utilized Jenkins for CI/CD pipelines, JIRA for tracking, Postman for API testing, Git for version control, and JUnit for automated testing
Technologies Used:
FeaturedProjects
FEATUREDPROJECTS
Showcasing innovative solutions built with cutting-edge technologies🚀 Next-generation applications pushing the boundaries of what's possible
Threadora - Event-Driven Microservices Platform
Event-driven microservices architecture (8 independently deployable services) with JWT auth, API Gateway rate limiting, and Resilience4j circuit breakers achieving 99.5% uptime on AWS. Redis caching and DB indexing cut API latency by 35%, with distributed tracing (Spring Cloud Sleuth + Zipkin) reducing mean-time-to-debug by 50%.
Threadora - Event-Driven Microservices Platform
Event-driven microservices architecture (8 independently deployable services) with JWT auth, API Gateway rate limiting, and Resilience4j circuit breakers achieving 99.5% uptime on AWS. Redis caching and DB indexing cut API latency by 35%, with distributed tracing (Spring Cloud Sleuth + Zipkin) reducing mean-time-to-debug by 50%.
Lit-Pick - Agentic RAG Book Recommendation Engine
Production RAG pipeline (LangChain + OpenAI embeddings + Chroma vector DB) behind an async FastAPI backend, handling 10K+ QPS at <150ms p99 - a 35% precision gain over collaborative filtering. Hybrid recommendation layer combines zero-shot sentiment classification with vector similarity search across 50K+ books, backed by MongoDB.
Lit-Pick - Agentic RAG Book Recommendation Engine
Production RAG pipeline (LangChain + OpenAI embeddings + Chroma vector DB) behind an async FastAPI backend, handling 10K+ QPS at <150ms p99 - a 35% precision gain over collaborative filtering. Hybrid recommendation layer combines zero-shot sentiment classification with vector similarity search across 50K+ books, backed by MongoDB.
Explore More Projects
Visit my GitHub to see more projects, contributions, and open-source work.
View GitHub🚀 WANT MORE?
Dive deeper into my digital universe. More projects, experiments, and innovations await!
EXPLORE GITHUBGITHUBTechnical Stack
Technologies & Tools
AI/ML & Agentic Systems
LangChain
OpenAI APIs
Hugging Face
PyTorchLanguages & Frameworks
Java
Python
JavaScript
TypeScript
Spring Boot / WebFlux / Cloud
Hibernate
FastAPI
React.js
Node.jsDatabases, Vector Stores & Messaging
MySQL
PostgreSQL
MongoDB
Redis
KafkaCloud & DevOps
AWS (EC2, S3, Lambda, RDS)
Azure
Docker
Kubernetes
CI/CD