Full-Stack Engineer • GenAI Engineer • React • Distributed Systems • Agentic AI • High-Scale Architecture
I build reliable, full-stack products end-to-end — from React frontends to high-performance backends — and increasingly, GenAI-powered engineering workflows and LLM-driven products. I'm passionate about:
- Full-Stack Development (React, TypeScript, FastAPI/Spring Boot APIs)
- GenAI Engineering (LLM internals, agentic workflows, RAG, prompt/context engineering)
- Backend Engineering (Java/Spring Boot, Python/FastAPI, Go)
- Distributed Systems & Messaging (Kafka, Redis, Pub/Sub)
- Multi-tenant Platforms & RBAC
- Cloud-Native & DevOps (Docker, CI/CD, Kubernetes, Google Cloud)
- Real-Time Processing & High Throughput APIs
Currently leading legacy mainframe → cloud-native modernization using GenAI-accelerated development tooling, alongside a foundation-first deep dive into LLMs (linear algebra → transformers → Karpathy's series).
🚧 Building now: full-stack products end-to-end (React + FastAPI/Spring Boot) powered by AI-assisted engineering workflows — work in progress.
| Category | Skills |
|---|---|
| Languages | Python, Go, Java, TypeScript, SQL |
| Frontend | React, TypeScript, Vue, HTML/CSS, REST/WebSocket integration |
| GenAI / LLM | LangChain, RAG pipelines, Agentic AI workflows, Prompt Engineering, LLM Orchestration, Multi-agent systems |
| Frameworks | FastAPI, Spring Boot, Spring Batch, Chi, Gin, Fiber |
| Cloud & Infra | Google Cloud (Cloud Run, BigQuery), Azure, Docker, Kubernetes, NGINX |
| Data Stores | Postgres, Redis, MongoDB, BigQuery |
| Messaging | Kafka, RabbitMQ, WebSockets |
| Observability | Grafana, Prometheus |
| Architecture | Multi-tenant, Distributed Systems, Microservices, Event-Driven, Polyglot Backend Systems |
- Modernizing legacy COBOL/mainframe pipelines to cloud-native services (Spring Batch → BigQuery), GenAI-assisted
- Building GenAI-powered developer tooling: RAG over codebases, AI-assisted PR review, multi-agent workflows
- Designing hybrid polyglot backend architecture — Spring Batch (batch), FastAPI (web layer), Go (event-driven on Cloud Run)
- Building a Hospital Management System (FastAPI, India-specific: DPDP Act 2023, ABDM/ABHA, GST billing)
- Structured LLM fundamentals track: linear algebra → neural nets → transformers → Karpathy's "zero to hero" series
- Building full-stack products (React + FastAPI/Spring Boot) using AI-assisted development workflows end-to-end — in progress
- Designing multi-tenant CRM architecture (Go + Vue) with fine-grained RBAC
- Building high-performance Redis caching layer
- CI/CD pipelines with GitHub Actions / Azure DevOps
| Project | Description |
|---|---|
| Hospital Management System | FastAPI-based HMS for Indian hospitals — patient mgmt, EHR, billing, pharmacy, lab, ABDM/ABHA & DPDP compliant |
| GenAI Dev Workflows | RAG over internal codebases, AI-assisted PR review, multi-agent orchestration for engineering leads |
| Event-driven Notification Engine | Kafka streaming workflows, retries, DLQ support |
| Redis Cache Middleware | High-performance TTL & invalidation wrapper for Go |
| College Management System | Multi-role access: admin, staff, student |
- Foundation First → the foundation is always the real skill
- Performance First → measure & optimize
- Clean Abstractions → domain-driven boundaries
- Scalability → services grow with business
- Resilience → retries, fallbacks, circuit breakers
- Security → least privilege + secure defaults
- Automation → CI/CD + GenAI-accelerated development, everywhere