Business Analyst
Contributed to the Amazon Agent Function Calling project across different scenarios like personal assistant, delivery manager and weather assistant etc.
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At Turing, I worked on AI training projects focused on improving Meta's Llama models through RLHF and Supervised Fine-Tuning (SFT). My work involved reviewing and annotating data, evaluating model responses, validating content quality, and identifying edge cases that could impact model performance. This gave me hands-on experience in creating high-quality training data and understanding how large language models learn and improve. What I brought to these projects was a strong research mindset and attention to detail. Before moving into AI, I worked in policy research and data analysis, where accuracy and evidence-based decision-making were essential. That experience helped me evaluate AI outputs critically, follow complex guidelines consistently, and contribute to building reliable datasets for model training and evaluation.
Contributed to the Amazon Agent Function Calling project across different scenarios like personal assistant, delivery manager and weather assistant etc.
Wrote responses to the user queries, rated the responses and fine tuned them as well.
Drew bounding boxes across different textbooks of various grades.
The project aimed to improve Meta's Llama AI models by making their responses more accurate, helpful, and aligned with user intent. It involved using human feedback and high-quality training data to strengthen instruction-following, reasoning, and safety capabilities, ensuring the models performed reliably across diverse prompts and tasks (image, video, texts, OCR and books).
Certification Course on Data Analytics and Generative AI, Data Analytics and Generative AI
MA Sociology, Sociology
Business Analyst
Research Associate