Nihal Soans is a Machine Learning and MLOps Engineer with nine years of experience specializing in computer vision, robotics, and production-grade ML deployment. He has delivered end-to-end systems—from converting 3D LiDAR scans into precise 2D floor plans and building 360° tri-camera prototypes for digital twins to integrating deep learning on edge accelerators with sub-30ms inference targets. Proficient in C/C++ and Python, Nihal designs custom vision architectures, camera calibration pipelines, and real-time capture systems using OpenCV, PyTorch/TensorFlow, ROS and GStreamer. He pairs hands-on algorithm development with practical MLOps work such as automated firmware/model rollout and synchronization across live camera networks, driving significant cost and bandwidth savings. Notably, his vendor evaluations and hardware selection work cut project expenses by over 80% while improving imaging performance—an uncommon blend of algorithmic depth and procurement-driven optimization. Based in Austin, he focuses his research and engineering on solutions that move academic ideas into real-world, operational products.
9 years of coding experience
8 years of employment as a software developer
BE, Computer Science & Engineering, BE, Computer Science & Engineering at St. Joseph Engineering College
12th; Pre-University, Computer Science, 12th; Pre-University, Computer Science at St. Aloysius (Deemed to be University)
Master of Science (M.S.), Computer Science, Master of Science (M.S.), Computer Science at University of Georgia - Franklin College of Arts and Sciences
10th, General, 10th, General at St Aloysius High School
Python code to extract depth and rgb data from rosbag
Contributions:30 commits, 2 PRs, 13 pushes in 4 years 3 months
pythonrosbag
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