• Developed reproducible Python and SQL evaluation pipelines for large graph datasets, ensuring data integrity and reliability.

• Conducted thorough dataset checks and experiment logging to enhance the trustworthiness of research outcomes.

• Engaged in failure-mode analysis to identify and mitigate potential issues in machine learning systems.

• Contributed to the acceptance of a full paper at ES 2026, focusing on reliable AI planning methodologies.

I focused on delivering cutting-edge AI products that streamlined document processing and enhanced efficiency.

• Developed agentic workflows to convert unstructured data into structured formats, improving data usability.

• Implemented caching, batching, and async worker queues, achieving a 38% reduction in inference costs.

• Ensured system reliability with circuit breakers, maintaining performance metrics for interactive workloads.