Arslan Shabbir

I’m a Junior AI Engineer focused on building end-to-end ML systems—from data collection and cleaning to training, deployment, and automation. My work is grounded in practical performance, scalability, and reliability.

In my role, I designed and implemented web scraping pipelines for large-scale dataset collection, followed by comprehensive data cleaning and preprocessing for model-ready inputs. I also developed AI automation workflows to streamline processing and model deployment pipelines, optimizing model performance metrics.

On the applied ML side, I trained and fine-tuned machine learning models for production use and deployed AI models into production environments to ensure scalability and reliability. Previously, as a Machine Learning Intern, I built an instance segmentation pipeline using YOLOv8 and integrated it into a Flask web application for real-time video stream processing and visualization.

I’m also motivated by research-driven product building, as seen in projects like an AI-Based Wardrobe Assistant and a symptom-based diagnosis system using LangChain and RAG. I enjoy collaborating with teams to refine hyperparameters, improve precision/recall, and deliver useful, context-aware AI experiences.