Agentic LLM Engineer for SEC Filing Intelligence
Opportunity Summary
You will engineer a research and automation platform that discovers, downloads, and processes SEC filings from multiple formats including HTML, XML, XBRL, PDFs, and text. The system will extract structured financial metrics, tables, disclosures, and other key details, then support question answering with retrieval augmented generation and filing-level citations. You will also create agent workflows for company and transaction research, plus pipelines for comparing disclosures across periods and securities. The role emphasizes production grade automation, validation and reconciliation checks, and integration with downstream dashboards or APIs.
Ideal Candidate
A senior engineer with deep experience in agentic LLM systems, document intelligence, and financial extraction. Strong at building reliable RAG with source references, and designing systems that can operate accurately at scale.
Project Insights
- Citation quality improves when you store chunk metadata tied to filing sections
- Structured outputs work best with strict schemas and post-processing checks
- Comparative analysis benefits from normalization across periods and entities
How to Prepare
- Include an outline of your pipeline from ingestion to extraction to cited QA
- Mention how you will validate extracted metrics and manage uncertainty
- Describe your approach to chunking, metadata, and retrieval for filing-level provenance
Typical Challenges
- Extracting consistent structured data from heterogeneous SEC document formats
- Reducing hallucinations through validation, reconciliation, and grounded citations
- Building scalable pipelines for large document collections and repeated updates
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Project Details
Skills & Requirements
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