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Freelancer Overview
I have experience working with AI and Generative AI systems involving data preparation, document processing, retrieval-augmented generation (RAG), and model evaluation. In my projects, I have handled large-scale datasets by performing data cleaning, annotation validation, text preprocessing, chunking, metadata tagging, and quality assurance to ensure high-quality training and retrieval data. I have built AI pipelines that process unstructured documents, generate embeddings, perform semantic search, and improve data relevance through retrieval and reranking techniques. These projects required strong attention to detail, consistency checking, and maintaining data quality standards across thousands of records. What sets me apart is my combination of AI engineering and data-focused skills. I have hands-on experience with Python, SQL, NLP, LLMs, RAG architectures, vector databases, and data analytics tools. I am comfortable reviewing large volumes of data, identifying labeling inconsistencies, creating annotation guidelines, and validating outputs against quality benchmarks. My background in building production-oriented AI systems has given me a deep understanding of how high-quality training data directly impacts model performance, accuracy, and reliability.