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While I do not have direct professional experience in AI training or data labeling, I have experience working with large datasets as part of my Data Engineering training and projects. I have performed data cleaning, validation, transformation, deduplication, schema standardization, and quality checks using Python, SQL, and PySpark. These tasks required careful attention to data accuracy and consistency, which are also important in data annotation and AI training workflows. I am a quick learner with strong analytical skills and am comfortable following detailed guidelines to produce accurate, high-quality work. I am proficient in using spreadsheets, databases, and programming tools to organize and validate data, and I am eager to apply these skills in AI data labeling, annotation, and model training projects.
I do not have prior professional experience in data labeling or AI training. However, I have experience working with large datasets as a Data Engineer using Python, SQL, and PySpark, where I performed data cleaning, validation, transformation, deduplication, and quality checks. I am confident in my ability to quickly learn data annotation guidelines and produce accurate, consistent work
Degree not specified
Worked on data engineering projects using Python, SQL, PySpark, Databricks, and AWS. Built ETL pipelines, performed data
