Web apps, dashboards, ERPs and API platforms
Full-stack product engineering
React, Next.js, Astro, Node, Nest, Express, Spring Boot, ASP.NET Core and FastAPI for end-to-end product builds.
Capabilities
A broad stack, organized around delivery.
The aim is not to collect tools. The aim is to choose the right layer, build the right architecture and keep the product maintainable after launch.
Focus areas
Where I create the most value.
From product UI to backend systems, CMS workflows, AI automation, mobile apps, infrastructure and security.
Shopify, WordPress, October CMS and Laravel
Commerce and CMS engineering
Storefronts, custom themes, CMS workflows, Shopify app work, B2B flows, WooCommerce and headless commerce migrations.
Classic systems to headless architectures
Legacy modernization
ASP.NET MVC, older CMS builds and monolithic workflows moved into cleaner API-driven systems with practical rollout paths.
RAG, NLP, agents and rapid prototyping
Applied AI and automation
Generative AI, ML pipelines, transcription, summarization, multi-agent automation and product prototypes that move from demo to deployment.
Deployment, observability and queues
Cloud, DevOps and scale
AWS, GCP, Docker, Kubernetes, Nginx, CI/CD, Kafka, RabbitMQ, caching and horizontal or vertical scaling decisions.
VAPT, OWASP and secure delivery
Security-minded engineering
Cybersecurity foundation from IIIT Delhi work, VAPT exposure, secure auth, hardening, risk assessment and production hygiene.
Stack
Tools I can actually ship with.
This list is intentionally practical: languages, frameworks, platforms and infrastructure used in real projects or serious builds.
Languages
- Java
- C#
- Python
- JavaScript
- TypeScript
- SQL
Web and backend
- React
- Next.js
- Astro
- Node.js
- Express
- NestJS
- Spring Boot
- ASP.NET Core
- FastAPI
- REST
- Microservices
Commerce and CMS
- Shopify
- Shopify apps
- Shopify B2B
- WordPress
- WooCommerce
- October CMS
- Laravel
- Hydrogen
- Magento
Mobile
- Flutter
- React Native
- Android
- iOS
AI and ML
- Generative AI
- RAG
- NLP
- Computer Vision
- TensorFlow
- PyTorch
- scikit-learn
- OpenAI APIs
- Anthropic APIs
Cloud and DevOps
- AWS
- GCP
- Docker
- Kubernetes
- Nginx
- CI/CD
- Linux
- Kafka
- RabbitMQ
- Caching
- Monitoring
Security
- VAPT
- OWASP
- Burp Suite
- Metasploit
- Wireshark
- Secure auth
- Risk assessment
Databases
- PostgreSQL
- MySQL
- MongoDB
Operating model
How I move from requirement to shipped system.
Fast does not mean careless. The process is designed to reduce risk without slowing down delivery.
01
Understand the system
Start with the users, workflows, constraints and deployment reality. The first job is to remove ambiguity.
02
Build the narrowest working version
Ship the core path early, prove it with real data and avoid over-designing around imagined edge cases.
03
Harden for production
Add auth, logging, error handling, deployment automation, caching and security controls where the risk justifies it.
04
Iterate with evidence
Use metrics, user feedback and code health to decide the next improvement instead of guessing.