Parsing Architect (AI) - SME

Education: Bachelor’s degree required
Experience: Minimum of 10 years of relevant experience

Position Summary

The Parsing Architect (AI) leads the design, scalability, and governance of enterprise document parsing and preprocessing systems that support autonomous agents and large language model (LLM) applications. This role establishes how large volumes of unstructured data are ingested, validated, parsed, and segmented for indexing and retrieval.

The architect evaluates document processing technologies and designs reliable, cost-effective pipelines that preserve document context and produce high-quality data for AI applications. The role works closely with data engineering, AI, security, governance, and compliance teams to ensure information remains traceable, secure, and accessible to authorized users.

Key Responsibilities

• Design enterprise architecture for ingesting, validating, parsing, and preprocessing large, multi-format document collections.

• Define scaling strategies that support high-throughput processing, reliability, and cost efficiency.

• Evaluate and integrate layout analysis engines, optical character recognition (OCR), multimodal vision-language models, and document processing APIs.

• Establish semantic data schemas, document segmentation and chunking rules, and metadata standards that preserve context across document libraries.

• Design retrieval infrastructure, including indexing strategies, vector database integrations, hybrid search, and graph-based retrieval-augmented generation (GraphRAG).

• Define and enforce access controls throughout document processing, indexing, and retrieval.

• Establish data lineage and traceability from source documents through processed content and retrieval outputs.

• Implement automated detection and filtering of personally identifiable information (PII) and protected health information (PHI).

• Partner with governance, security, and compliance teams to translate enterprise requirements into architecture standards and technical controls.

• Monitor and improve parsing accuracy, contextual completeness, processing performance, and operating costs as AI requirements evolve.

Required Qualifications

• Minimum of 10 years of relevant experience in data engineering, enterprise architecture, document processing, AI infrastructure, or related technical disciplines.
• Experience designing scalable architectures for large volumes of unstructured and multi-format data.
• Experience evaluating and integrating document parsing, OCR, layout analysis, and AI-based document processing technologies.
• Understanding of LLM data preparation, including semantic schemas, chunking strategies, metadata enrichment, and context preservation.
• Experience with indexing, vector databases, hybrid search, and graph-based retrieval architectures.
• Experience incorporating access controls, data lineage, and sensitive data filtering into enterprise data pipelines.
• Ability to communicate architectural decisions and collaborate across engineering, AI, security, governance, and compliance teams.