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Schedule Interview NowMy name is Bilal A. and I have over 2 years of experience in the tech industry. I specialize in the following technologies: pandas, Computer Vision, Keras, Machine Learning Model, Machine Learning, etc.. I hold a degree in Bachelor of Science (BS), Master of Science (MS). Some of the notable projects I've worked on include: Object Detection and Tracking, Calibrating football fields using keypoints, Real time object detection on the road., YOLO Inference: PyTorch vs TensorRT Performance Comparison, Waste detection, etc.. I am based in Lahore, Pakistan. I've successfully completed 10 projects while developing at Softaims.
I specialize in architecting and developing scalable, distributed systems that handle high demands and complex information flows. My focus is on building fault-tolerant infrastructure using modern cloud practices and modular patterns. I excel at diagnosing and resolving intricate concurrency and scaling issues across large platforms.
Collaboration is central to my success; I enjoy working with fellow technical experts and product managers to define clear technical roadmaps. This structured approach allows the team at Softaims to consistently deliver high-availability solutions that can easily adapt to exponential growth.
I maintain a proactive approach to security and performance, treating them as integral components of the design process, not as afterthoughts. My ultimate goal is to build the foundational technology that powers client success and innovation.
Main technologies
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Machine Learning 1
We started with object detection using YOLO on images, webcam, and videos. Then we added tracking, so that each object gets a consistent ID across frames. Then we counted unique objects by maintaining
Part 1: Explored training YOLOv11 on a custom dataset for precise keypoint localization, transforming single-pixel landmarks into bounding boxes for better accuracy under real-world conditions like ca
Real time object detection to identify vehicles and other objects on the road.
Compared YOLO model performance between PyTorch and TensorRT for real-time object detection, finding PyTorch surprisingly outperformed TensorRT in initial tests for instance segmentation tasks. Planni
1️⃣ The first model detects only garbage — ideal for quick identification and monitoring in streets, parks, and other public spaces. 2️⃣ The second model classifies different types of waste — useful
Bachelor of Science (BS) in Electrical engineering
2012-01-01-2016-01-01
Master of Science (MS) in Machine Learning
2018-01-01-2022-01-01