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TextI am an AI/ML engineer with hands-on experience designing and deploying large-scale computer vision and LLM-powered systems, where high-quality training data and robust data pipelines are critical. My work has focused on building end-to-end machine learning platforms, with a strong emphasis on data-centric iteration, model evaluation, and reducing false positives through improved data labeling and annotation strategies. I have led initiatives to standardize AI-assisted workflows and have developed evaluation frameworks using graph-theoretic metrics for LLMs. My technical toolkit includes Python, AWS, Docker, MLflow, and advanced inference optimization tools like TensorRT and Triton. I am passionate about ensuring data quality and reliability in AI systems, and have experience working with multimodal and real-time perception data in domains such as computer vision, NLP, and embedded AI.
Asking Large language models questions about research papers and correcting their answers, focusing on factuality and interpretation of the results in the broader part of the literature.
Doctor of Philosophy, Information Geometry
MicroMasters, Statistics and Data Science
AI Engineer
R&D Intern, Artificial Intelligence
