Parsing Architect (AI) - Senior

Develop and maintain text extraction and preprocessing pipelines for PDFs, presentations, web documents, and files containing charts and tables.

Preserve document structure during extraction, including heading hierarchies, bulleted lists, and relationships between content sections.

Transform extracted content into clean, consistently formatted Markdown suitable for embedding and downstream AI processing.

Implement advanced chunking strategies, including parent-child document relationships and sliding-window chunking, to maintain context and improve retrieval.

Use tools such as Docling and layout-aware open-source libraries to process complex documents.

Implement multimodal, vision-based methods for extracting tables and interpreting document layouts.

Write specialized parsing code to extract data embedded in tables while preserving relationships between headers, rows, and values.

Enrich text chunks with metadata, including source lineage, temporal information, and classification levels, to support precise filtering and hybrid search.

Integrate parsing outputs with vector databases, LLM applications, and RAG frameworks.

Benchmark and tune pipelines for extraction quality, retrieval accuracy, embedding effectiveness, and context window utilization.

Collaborate with AI researchers, data engineers, and ML platform teams to resolve parsing issues and improve pipeline performance.

Minimum of 8 years of relevant experience in software engineering, data engineering, document processing, AI data pipelines, or related technical disciplines.

Experience developing document parsing, text extraction, and preprocessing workflows for unstructured data.

Ability to preserve semantic structure and produce clean, structured Markdown from complex documents.

Experience implementing chunking strategies that maintain context, including parent-child relationships and sliding windows.

Experience with document processing tools such as Docling or comparable layout-aware libraries.

Experience extracting tabular data from complex documents using programmatic and vision-based techniques.

Understanding of embeddings, vector databases, LLM applications, and RAG workflows.

Experience applying metadata enrichment to support source traceability, filtering, and hybrid search.

Ability to evaluate pipeline quality and troubleshoot issues affecting retrieval accuracy and downstream AI responses.

Strong collaboration and communication skills.