Elizabeth Khan

Elizabeth focuses on delivering measurable business value through the design, deployment, and operationalization of machine learning and Generative AI systems. Her expertise spans Generative AI, MLOps, LLMOps, and large-scale model deployment, with a strong emphasis on building production-ready ML and GenAI solutions rather than experimental prototypes. She has led and contributed to advanced document extraction use cases, as well as more sophisticated GenAI applications that move from proof-of-concept to enterprise adoption.

Her background includes advanced analytics, data science, data engineering, and ML platform development across industries including healthcare, energy, manufacturing, federal, logistics, entertainment, and aerospace. Elizabeth has led projects ranging from retrieval-augmented generation (RAG) systems to compound AI and agentic architectures, helping organizations design scalable, reliable, and governed AI systems.

Previously, Elizabeth served as a technical lead in machine learning at Lovelytics, where she played a key role in optimizing ML and MLOps practices for clients within the Databricks ecosystem. Prior to that, she contributed to client success at Accenture by developing prototype analytics products, building robust data pipelines, designing insightful Tableau dashboards, and creating customized machine learning models.

In her free time, Elizabeth enjoys exploring Richmond, VA’s vibrant music and art scene, hiking outdoors, and traveling to gain new perspectives.