RAG
The intent of the project was to build an LLM and train it to provide accurate and complete responses.
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I am an experienced AI data trainer and annotator with over three years of hands-on expertise in supporting the development of large language models and multimodal AI systems. My background includes supervised fine-tuning (SFT), reinforcement learning with human feedback (RLHF), prompt engineering, and rigorous evaluation of AI outputs to ensure quality, safety, and alignment. I have worked extensively on data annotation and curation across text, image, video, and audio modalities, with a strong focus on bias reduction, data quality, and reliable AI performance in both multilingual and STEM-oriented domains. My experience spans object detection, bounding box annotation, sentiment analysis, content categorization, and professional scenario modeling for AI. I have contributed to projects involving computer vision, natural language processing, and cross-lingual model training, consistently maintaining high standards for dataset integrity and annotation quality. I am adept at collaborating with cross-functional teams, structuring datasets for optimal training workflows, and providing actionable feedback to drive continuous model improvement.
The intent of the project was to build an LLM and train it to provide accurate and complete responses.
The intent of the project was to teach an LLM how to describe the contents of an image.
The intention of the project was to teach an LLM how to perform computer actions.
Master of Business Administration, Business Administration
Bachelor of Technology, Computer Science
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