Shailesh Sridhar is an AI/ML engineer with 8 years of experience building production ML systems and conducting interpretability research, currently focused on transformer-based architectures for vision and language. He bridges research and engineering—publishing first-author work and earning a thesis runner-up award while delivering quantized edge models, semantic search, and containerized ML services in production. Recent projects include improving 3D lung CT malignancy prediction via hybrid foundation-model fine-tuning and developing lightweight, efficient pipelines for face authentication and OCR at scale. His research on mitigating missingness bias in feature attribution and uncovering spurious behaviors in vision-language models reflects a deep interest in AI transparency. Based in Chicago, he thrives on applying interpretability to healthcare and sustainability problems and contributing to open-source tooling. A detail often overlooked: he combines statistical, optimization, and systems skills to push models from explainable prototypes into robust, resource-constrained deployments.
8 years of coding experience
3 years of employment as a software developer
Bachelor of Technology, Computer Science, Bachelor of Technology, Computer Science at PES University
10th grade CBSE, 10 CGPA, 10th grade CBSE, 10 CGPA at GEAR Innovative International School
12th Grade PUC, 92.6%, 12th Grade PUC, 92.6% at The Amaatra Academy
Master of Science and Engineering, Data Science, Master of Science and Engineering, Data Science at University of Pennsylvania
Contributions:1 review, 2 commits, 1 PR in 4 years 11 months
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