Aravind Mysore

Background

About Aravind Mysore

Carnegie Mellon-educated Machine Learning Engineer with specialized expertise in Computer Vision, NLP, and Generative AI. I bring hands-on experience developing production-ready ML solutions that deliver measurable business impact: Reduced document processing time by 83% at Bank of New York's AI Hub by creating an LLM-powered information extraction pipeline with 90% recall/95% accuracy Engineered 3D reconstruction systems and trained edge-device CV models using Gaussian Splatting and NVIDIA Omniverse technologies for warehouse automation Led Computer Vision and Pattern Recognition initiatives at a sports-tech startup, implementing state-of-the-art deep learning models (YOLO, BlazePose, Mask R-CNN) for performance analysis applications My technical toolkit includes PyTorch, TensorFlow, and the complete ML engineering stack, complemented by research in LLMs, graph neural networks, and simulation modeling. Currently serving as a Teaching Assistant for Generative AI at Carnegie Mellon, where I help develop coursework on cutting-edge LLM and diffusion model architectures. I thrive at the intersection of research innovation and practical implementation, consistently delivering solutions that balance technical excellence with business requirements. My experience spans synthetic data generation, model optimization for edge devices, and end-to-end ML pipeline development across multiple domains. Open to discussing challenging ML Engineering roles where I can leverage my expertise in deep learning, computer vision, and NLP to drive meaningful innovation. Let's connect!