P-01
Systems before screens
Every product starts with data models, APIs, failure handling, and clear system boundaries. The UI is the last mile, not the foundation.
▸ SYSTEM ONLINE — PORTFOLIO v2.0
Vamshi Krishna.
Backend Engineer.
Java & AI Automation.
I build production-grade backend systems, offline-first mobile platforms, and AI automation workflows with Java, Spring Boot, PostgreSQL, Redis, Docker, and n8n.
Java / Spring BootPostgreSQL / Redisn8n / GeminiReact Native / Next.jsDocker / CI/CD
01 ▸ OPERATING PRINCIPLES
I build software the way production systems demand it.
Final-year B.Tech Computer Science student specialising in Artificial Intelligence & Machine Learning, focused on backend engineering, Java Spring Boot systems, and AI automation workflows.
I build projects as production systems — with caching, rate limiting, database design, API documentation, CI/CD, testing, deployment, and failure handling built in from day one.
Currently seeking software engineering internships and backend / AI automation roles where systems thinking matters.
Software Engineering InternshipsBackend Engineering InternshipsAI Automation RolesStartup Engineering RolesRemote Internships
P-02
Production is the standard
Caching, rate limiting, authentication, observability, and deployment are not afterthoughts — they are designed in from day one.
P-03
Automate the repeatable
If a workflow runs twice, it can become an automation pipeline. AI agents and n8n flows handle the routine so humans handle judgment.
P-04
Measure, then optimize
Benchmarks, latency numbers, cache hit rates, and tests are stronger than assumptions. Decisions ride on numbers, not instinct.
02 ▸ CAPABILITY MATRIX
One stack, from backend systems to AI automation.
A practical engineering toolkit focused on APIs, data, caching, automation, and production delivery.
Languages
JavaPythonJavaScriptTypeScriptSQLHTML5CSS3
The foundation — strong typed, scripted, and query languages.
Backend
Spring BootSpring MVCSpring SecurityOAuth 2.0JWTREST APIsHibernate / JPAMavenNode.js
Production-grade services designed for correctness, load, and change.
Databases
PostgreSQLMySQLRedis
Storage chosen per access pattern — relational durability, cache speed.
Frontend
ReactNext.jsTailwind CSSReact Native
Modern interfaces that respect performance budgets.
AI / Automation
OpenAI APIGemini APIn8nLangChainRAG Pipelines
LLMs and workflow engines wired into real business pipelines.
DevOps
DockerGitHub ActionsRailwayVercel
Reproducible environments and shippable artifacts.
Tools
GitPostmanSwagger / OpenAPIIntelliJ IDEAVS Code
Day-to-day engineering toolkit for building and debugging.
Concepts
MicroservicesCachingRate LimitingDatabase IndexingAPI DesignSystem Design
Principles that guide system design decisions.
▸ ENGINEERING PHILOSOPHY
I don't build demo projects.
I build systems that can be tested,
deployed, measured, and explained.
03 ▸ SELECTED SYSTEMS
Projects engineered as products, not demos.
01 ▸ MOBILE + BACKEND SYSTEM
MetroNexis
Production-Grade Metro Navigation Platform for India
An offline-first metro navigation platform for Indian commuters, combining a React Native mobile app with a Spring Boot backend to deliver fast route planning, fare calculation, ticketing, wallet, service alerts, and multi-city metro support.
- Stations modeled
- 290
- Automated tests
- 152+
- Flyway migrations
- 18
- Cities live
- Delhi + HYD
Java 21Spring Boot 3.3.5PostgreSQL 16Redis 7React Native 0.81.5Expo SDK 54TypeScriptMMKVDockerRailwayGitHub ActionsSwagger / OpenAPIFlywayJWT
02 ▸ BACKEND SYSTEM
Distributed URL Shortener
Production-Grade URL Shortening Platform
A high-performance Spring Boot URL shortener with Redis caching, PostgreSQL persistence, Base62 batch ID generation, JWT authentication, rate limiting, asynchronous analytics, and CI/CD.
- Redirect p99
- 4.13ms
- Cache hit rate
- 99.98%
- Throughput
- 100+ RPS
- ID block size
- 1,000
Java 17Spring Boot 3PostgreSQL 16Redis 7Docker ComposeGitHub ActionsFlywayJJWTBCryptMicrometerPrometheusJUnit 5MockitoTestcontainers
03 ▸ AI AUTOMATION
LeadOps AI CRM
AI-Powered Lead Qualification & Outreach Automation
A production-tested AI automation pipeline that captures leads, scores them with Gemini, generates personalized outreach emails, stores structured CRM data, and routes hot leads automatically.
- Response time
- <10s
- AI score range
- 1–10
- Lead routing
- Hot/Warm/Cold
- Manual work
- 0
n8nGoogle Gemini APIGoogle FormsGoogle Sheets APIGoogle Apps ScriptGmail SMTPWebhooksJavaScriptService Account CredentialsConditional Routing