AI Engineer

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Experience: 3+ yrs

Location: Chicago, Illinois, United States

Job Type: Full-time

We are looking for an experienced AI Engineer to design and build the intelligence layer across a document-to-return workflow. The role focuses on developing production-grade AI systems that transform complex financial and tax documents into reliable, structured data and support tax professionals in identifying missing information, inconsistencies, and potential errors.

This is a hands-on engineering role focused on document intelligence, LLMs, extraction, agentic systems, evaluation, and human-in-the-loop workflows. The ideal candidate combines strong technical skills with a high bar for accuracy, traceability, observability, and production reliability.

Key Responsibilities

• Build production systems for document classification, OCR, parsing, and structured data extraction.
• Process PDFs, scanned documents, tax forms, financial statements, receipts, and other unstructured financial information.
• Design extraction workflows that preserve source context, handle ambiguity, and route low-confidence results for human review.
• Develop LLM-powered document understanding and intelligent extraction capabilities.
• Build AI agents that analyze completed returns against source documents and relevant tax context.
• Identify missing information, inconsistencies, potential errors, and other issues and present findings clearly for professional review.
• Design human-in-the-loop workflows that provide appropriate confidence signals, source citations, review controls, and correction mechanisms.
• Build scalable evaluation frameworks for structured and unstructured document-processing systems.
• Define evaluation datasets, ground-truth labels, scoring methodologies, benchmarks, and statistical analysis approaches.
• Establish observability and feedback systems to measure extraction quality, model performance, and user outcomes.
• Monitor production AI workflows and continuously improve accuracy, reliability, and coverage.
• Identify high-effort manual steps within document and return-preparation workflows where AI can provide measurable value.
• Collaborate with engineering and domain experts to translate real-world workflow requirements into reliable AI systems.
• Establish reproducible testing and evaluation processes for models, agents, and extraction pipelines.
• Contribute to expanding AI capabilities across document processing, return review, and professional workflows.

What Makes You a Great Fit

• 3+ years of experience building production AI, machine learning, or intelligent automation systems.
• Strong hands-on experience with document OCR, document understanding, parsing, and structured data extraction.
• Experience working with PDFs, forms, scanned documents, financial documents, or other complex unstructured data.
• Strong understanding of LLM-based document processing and modern AI techniques for extraction and reasoning.
• Experience designing and implementing evaluation frameworks for AI or machine-learning systems.
• Strong knowledge of evaluation datasets, ground-truth labeling, scoring methodologies, benchmarking, and statistical analysis.
• Experience building or working with agentic AI systems and human-in-the-loop workflows.
• Strong understanding of observability, reproducibility, model monitoring, and production AI reliability.
• Proficiency in Python and experience building scalable production systems.
• Strong analytical and problem-solving skills with exceptional attention to accuracy and detail.
• Ability to design AI workflows where outputs are traceable, auditable, explainable, and actionable.
• Strong product and engineering judgment with a practical, outcome-oriented approach to AI development.
• Comfortable working closely with domain experts and incorporating real-world feedback into AI systems.
• Strong bias toward shipping, experimentation, measurement, and continuous improvement.
• Comfortable operating in a small, high-ownership environment where engineering and product responsibilities are closely connected.