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Schedule Interview NowWorking at Softaims has been an experience that continues to shape my perspective on what it means to build quality software. I’ve learned that technology alone doesn’t solve problems—understanding people, processes, and context is what truly drives innovation. Every project begins with a question: what value are we creating, and how can we make it lasting? This mindset has helped me develop systems that are both adaptable and reliable, designed to evolve as business needs change. I take a thoughtful approach to problem-solving. Instead of rushing toward quick fixes, I prioritize clarity, sustainability, and collaboration. Every decision in development carries long-term implications, and I strive to make those decisions with care and intention. This philosophy allows me to contribute to projects that are not only functional, but also aligned with the values and goals of the people who use them. Softaims has also given me the opportunity to work with diverse teams and clients, exposing me to different perspectives and problem domains. I’ve come to appreciate the balance between technical excellence and human-centered design. What drives me most is seeing our solutions empower businesses and individuals to operate more efficiently, make better decisions, and achieve meaningful outcomes. Every challenge here is a chance to learn something new—about technology, teamwork, or the way people interact with digital systems. As I continue to grow with Softaims, my focus remains on delivering solutions that are innovative, responsible, and enduring.
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I developed an AI-Powered Song Finder application deployed on AWS EC2 that combines OpenAI GPT-4o with the Spotify API for personalized music recommendations. Features include secure user authentication with bcrypt hashing, SQLite storage, and persistent login via encrypted cookies. Users describe their music preferences in natural language, which GPT-4o interprets to identify themes, then the system searches Spotify's catalog across North American markets to deliver targeted song recommendations with playable previews.
I developed a Streamlit RAG application deployed on AWS EC2 that combines Ollama (Gemma 2B) with ChromaDB for intelligent Q&A. Features include secure user authentication with bcrypt hashing, SQLite storage, topic-based content organization with access controls, and document processing for PDFs/DOCX. Users can upload documents, search semantically, get context-aware answers, and share private topics through encrypted codes, demonstrating my Python and ML development expertise.
The primary goal of this project was to develop a solution to detect deepfake videos, which has recently become a major issue due to the rapid advancement of Generative AI. My approach for this project involved the use of ResNeXt50 which is used to extract facial key points from the video frames, and LSTM which is used to analyze temporal patterns at each time step (frame-by-frame). This solution could predict videos up to 10 seconds in length at 30 frames per second with varying backgrounds and decent accuracy.
A real-time chat application built using Python's Django framework and the Channels library for handling WebSockets. The front end is developed using vanilla JavaScript and styled with Tailwind CSS.
This was my internship project where I had to train a model on Spotify datasets and then deploy that model on an application that was made using Flask. The application had a form where the usage data of a Spotify user will be entered and based on that user data it will be predicted whether that user will skip a certain song or not
Bachelor of Science (BS) in Computer science
2019-01-01-2023-01-01