Principal Engineer
CurrentAs a principal engineer, helped design and build various retrieval and augmented generative AI applications. The AI app sourced knowledge articles, training videos, and web pages that improved the efficiency to the call center by reducing repeated field calls with improved accuracy. The source data was ingested into various chunking strategies, metadata, and tagging. This was then used to create vector embeddings along with various ranking techniques to improve search retrieval accuracy. Leveraging frameworks like LangChain helped with the initial buildout of the orchestration between vector search, LLMs, prompt chaining, and agents. Further expanding the orchestration capabilities, designed a Generative AI orchestration service that scaled based on usage built on top of Kubernetes that provided the orchestration flexibility to easily expand various LLMs and loosely coupled the vector stores . It also provided the ability to provide authorizations between the application and the Embeddings within the vector store. Leveraged an evaluation framework that improved the search retrieval accuracy based on adjusting the chunking strategy, number of sources, search algorithm, and feedback annotations. Developed various techniques to use LLMs to help facilitate in the orchestration of search and retrieval accuracy. * Design, developing, and maintaining AI Generative AI systems for call center to improve accuracy, and efficiency. * Data collection (website, video, audio, documents), preprocessing (production grade pipeline), process flow design (embedding model, LLM model like GPT3.5/GPT4) and comparison, training and tuning LLMs, and deploy models to prod. * Contributed to the AI governance and council to help provide recommendations, principal design decisions, and learnings. * Leveraged LangChain for experiments and evaluation framework for different versions of GPT for response accuracy.