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Freelancer Overview

I am a computer scientist with a strong background in machine learning engineering and large-scale data systems, specializing in building and optimizing AI training pipelines. My experience includes designing distributed model training infrastructure at Google, developing robust MLOps workflows for model validation and deployment, and collaborating closely with ML researchers to productionize models for real-world applications such as recommendation systems. I have hands-on expertise with Python, PyTorch, TensorFlow, Apache Spark, and data engineering tools, as well as direct contributions to open-source projects like Apache Airflow for dynamic data workflows. My work spans domains such as computer vision, NLP, and real-time anomaly detection, where I have led teams in developing end-to-end platforms for processing, labeling, and monitoring large volumes of sensor and event data. I am passionate about ensuring high-quality training data and building scalable, reliable systems to support AI innovation.

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