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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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This project developed a Vision Transformer model to classify satellite images for Amazon deforestation monitoring. We achieved strong performance, as evidenced by the F-beta scores (around 0.85) and decreasing loss.
AI-powered banking chatbot, featuring domain-adapted finetuning for specialized expertise, Retrieval-Augmented Generation (RAG) for accurate answers, and robust guardrails for secure, safe interactions.
This project demonstrates object detection on car images using the YOLO (You Only Look Once) model. It involves data preprocessing, model training, and object detection with YOLO.
Social Media Content Generation Automation
Deep learning model using MobileNet to classify 38 plant diseases with 96%+ accuracy. Ideal for smart agriculture apps.
Bachelor of Engineering (BEng) in Software Engineering