"I enjoy creating software, trying new ideas, and polishing them into meaningful solutions that feel simple, reliable, and useful."
I’m a Computer Science Engineering undergraduate (Class of 2027) who genuinely enjoys building clean, reliable, and efficient software. Over time, I’ve explored different areas of tech, including software development, AI/ML, open-source, and software testing, which has helped me grow into a more well-rounded engineer.
Currently, I work as a Software Tester at Nothing Technology Limited, where I focus on User Acceptance Testing (UAT) and making sure products feel stable and polished before they reach users. I’ve also had the opportunity to work as a Machine Learning Intern at Infosys Springboard, where I built AI-based solutions for agriculture, and as a Web Development Intern at IBM SkillsBuild.
Outside of internships, I enjoy contributing to open-source projects, solving real issues, learning from other developers, and improving tools that people actually use. It has been one of the best ways for me to improve my debugging skills, understand production-level code, and collaborate in real engineering workflows.
I’m always working on improving my problem-solving skills through DSA, while continuing to explore how AI-driven development and thoughtful user-focused design can come together to create better software.
- 🚀 Deep Diving into AI-Powered Applications: Integrating Large Language Models (LLMs) with RAG systems to create smarter tools.
- 🛡️ Software Reliability: Applying testing methodologies learned at Nothing to personal development projects.
- 🤝 Open Source: Actively contributing to model metadata and AI tooling at
anomalyco/models.dev.
A premium wallpaper platform for discovering vibe-based collections.
- Stack: React, Vite, Unsplash API, Cloudinary, Firebase Auth.
- Key Feature: Responsive UI with curated drops and exclusive uploads.
An AI-powered web app for detecting rice and pulse crop diseases through CNN-based image analysis.
- Stack: Python, PyTorch, CNN, Streamlit.
- Key Feature: High-accuracy automated diagnostic system for farmers using deep learning.
A RAG-based AI system for querying PDF documents with semantic context.
- Stack: LangChain, Vector Database, TinyLlama, Embeddings.
- Key Feature: Intelligent document interrogation with state-of-the-art semantic search.
- Software Tester @ Nothing Technology Limited (Oct 2024 – Present)
- Machine Learning Intern @ Infosys Springboard (Nov 2025 – Jan 2026)
- Web Development Intern @ IBM SkillsBuild (July 2025 – Aug 2025)
- Open Source Contributor actively contributing to opensource projects
I'm always open to collaborating on interesting projects, discussing software testing, or chatting about the latest AI trends.
- 📧 Email: mohdsaifansari8888@gmail.com
- 🔗 LinkedIn: seffhunnn
- 🐦 GitHub: seffhunnn
This is a personal portfolio repository showcasing my work, projects, and professional journey.
Made by Saif