Lead research at the intersection of industry-scale data and academic rigor in Document Understanding, designing AI-native agentic systems to unlock insights from billions of historical and genealogical records.
Responsibilities
- Innovate with State-of-the-Art AI: Implement cutting-edge AI solutions for key Document Understanding tasks such as OCR/HTR, transcription, Named Entity Recognition (NER), Relation Extraction (RE), Coreference Resolution, Summarization, and Knowledge Graphs working with diverse genealogical and historical collections spanning newspapers, city directories, family history books, and vital records (i.e., birth, marriage, & death records).
- Architect Agentic Systems: Design and implement multi-agent workflows using frameworks like LangChain, LangGraph, CrewAI, AutoGen, AgentCore, Strands, Google ADK, A2A, etc. to automate complex multi-step reasoning tasks in historical document analysis and information extraction.
- Analyze and Optimize Multi-Modal Models: Evaluate the performance of multi-modal AI & LLMs such as GPT, Gemini, Claude, Llama, and Qwen for zero-shot and few-shot scenarios in comprehensive document understanding.
- Natural Language Processing (NLP): NER, Relation Extraction, Coreference Resolution, Entity Resolution, Knowledge Graphs (Neo4j), spaCy, NLTK, BERT.
- Computer Vision (CV): Apply expertise using models like YOLO, Nougat, DONUT, OpenCV, etc. to perform layout analysis, identifying text blocks, headers, tables, and deeply nested lists.