Pioneering multi-modal capabilities in a model's training pipeline, co-led a cross-functional team to successfully integrate Visual Question Answering (VQA) and Retrieval-Augmented Generation (RAG) methods. This strategic application of advanced research led to a 20% increase in model accuracy scores and significantly enhanced its capabilities across all three integrated modalities. 

By leveraging Retrieval-Augmented Generation (RAG) and Reinforcement Learning from Human Feedback (RLHF), increased the accuracy of Oracle’s internal model, which had been underperforming on complex, domain-specific queries. Sourced and processed real-world PDFs, and authored challenging prompts to test and improve the LLM’s performance. This implementation boosted model accuracy on a new set of metrics, significantly improving performance on knowledge-intensive tasks and resulting in a reduction in hallucinated content.

Enhanced the LLaMA model's image and pixel-level comprehension by applying Direct Preference Optimization (DPO) and Supervised Fine-Tuning (SFT) methods. Collaborated with the Meta engineering team to meticulously collect diverse, real-world image data for model training. This led to an 18% increase in the model's accuracy, resulting in a notable improvement in correct image-based responses.

Improved the factual accuracy of an LLM model by implementing a Reinforcement Learning from Human Feedback (RLHF) framework. Engineered data pipelines to gather and process real-world data, ensuring the model's alignment with client-specific requirements. This enhanced the model's performance by 19%, resulting in a direct increase in factual accuracy and a reduction in hallucinated content.

To ensure data integrity for critical business operations, engineered and maintained data APIs and ETL pipelines. Optimized data flow with robust API integrations, which enhanced data handling and reliability. This resulted in an improvement in data processing efficiency by 25% and a 15% reduction in data-related errors.