I am an SAP Fiori Developer with over 2 years of experience in developing enterprise-grade Fiori applications, coupled with OData, Node.js, and Python-based backend development.
Background
Area of Interest
- Competitive Programming
- Web Development
- Web Scraping
- Data Structures & Algorithms
- Machine Learning
Education
B.Tech (ECE)
Indian Institute of Technology, Roorkee
Intermediate (Class XII)
D S Science Academy, Gangapur City (RBSE)
Skills
Languages
- C++
- JavaScript
- TypeScript
- Python
- ABAP
- SQL
- HTML5
- CSS3
Frameworks & Libraries
- Next.js
- React
- Node.js
- TailwindCSS
- Express.js
- RESTful APIs
- OData
- Recoil
- Redux
- Zustand
- SAP UI5
Cloud, DevOps & Databases
- PostgreSQL
- Supabase
- HANA DB
- AWS EC2
- Render
- Vercel
- Firebase
- Docker
- Git
- GitHub
- Azure DevOps (ADO)
Emerging Tech & Enterprise (SAP)
- Generative AI
- Large Language Models (LLMs)
- Prompt Engineering
- SAP BTP
- CAP
- CDS Views
- S/4HANA RISE
Work Experience
SAP Fiori Developer
V3iT Consulting Pvt. Ltd.
Enterprise S/4HANA Cloud RISE & Fiori Application Migration
Key Highlights
• Converted over 80 ABAP program-based SQ01 queries to Analytical Core Data Service (CDS) Views.
• Converted CDS Views to Fiori Elements List apps on Business Application Studio (BAS) using Python and AI automation pipeline.
• Performed SPAU, SPDD, and SPAU_ENH reconciliations for the latest S/4HANA Cloud RISE system migration.
• Created Python script using Playwright to automate the task of adding over 2000 catalogs and tiles, saving hundreds of hours.
• Solved ad-hoc problems while migrating an old 2021 S/4HANA system to the latest 2025 S/4HANA Cloud with RISE system.
• Provided technical implementation guidelines for business functions related to Supply Chain Management (SCM) projects.
• Created technical specifications, ADO testing steps, and quick reference documents, and tested the Fiori apps from frontend to backend.
• Automated the task of creating User-Specific manuals for User Acceptance Testing (UAT) using Python.
Technologies
- SAP Fiori
- Python
- AI Automation
- SAP BTP
- CAP
- CDS Views
- S/4HANA RISE
- Playwright
- ADO
Projects
ChessRev - Free Chess.com games review
Description
• Created a web app using NextJS and tailwind to analyze Chess.com games for free using Stockfish 18 Lite.
• It has features like importing recent games of a profile on Chess.com, adding any game from PGN or url of any Chess.com game.
• It displays the top engine lines at each move, shows the move classification and a coach commentary based on the tactics of the position.
• The Stockfish code runs on the client side while the classification and commentary logic runs on server side using NextJS Edge runtime.
Stack & Tools
- NodeJS
- NextJS
- TailwindCSS
- Stockfish 18 Lite
- Zustand
VLR Duel Game
Description
• Created a WebSocket-based real-time 1v1 multiplayer game using Next.js, Tailwind CSS, Supabase Realtime, and Python.
• Implemented Login and User Management, Matchmaking Logic, Game Logic, and Score Calculation logic from scratch.
• Added database-level restrictions to avoid Race Conditions, Real-time and Low-latency database updates.
Stack & Tools
- Next.js
- PostgreSQL
- Supabase
- Python
- WebSockets
- Tailwind CSS
Cognitive Therapy Chatbot
Description
• Designed a fully functional web application containing a Chatbot trained to provide mental health-specific guidance using React, Firebase, Tailwind CSS, and the OpenAI API. Deployed the application to AWS EC2 Lambda instance with PM2 NPM package.
• Created a recommendation engine using Python, which recommends articles and blogs by analyzing the user conversation.
• Implemented community forum page with Post, Comment, Upvote, Downvote, Edit, and Delete functionalities from scratch.
• Created a web scraper using Python to scrape the latest news, blogs, articles, and content within 1000ms.
Stack & Tools
- React
- Python
- Node.js
- Firebase
- AWS EC2
- Tailwind CSS
- OpenAI API
Dynamic Maze Generation Game
Description
• Developed a Python-based maze game capable of generating mazes of desired size and difficulty (level), analyzing their difficulty and solving them.
• Kruskal's algorithm was used to generate the maze data.
• Machine learning models like Logistic regression and SVM were used to predict the difficulty of the generated mazes.
• PyGame library was used to create the graphical interface for the game.
Stack & Tools
- Python
- Scikit-learn
- Numpy
- Pandas
- Pygame
- Graphs
- Algorithms
Clustering Queries For Enhanced Customer Support
Description
• Utilized NLTK library in Python for processing of customer queries dataset.
• KMeans and GMM algorithms were used to cluster queries of similar types.
Stack & Tools
- Python
- NLTK
- NLP
- Clustering
- Sentiment Analysis
Resume
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