W
AI Engineer
Weekday 1
2 hours ago
Full-time
On-site
Chicago, Illinois, United States
This role is for one of the Weekday's clients
Salary range: Rs 100000000 - Rs 200000000 (ie INR 100-200 LPA)
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.
Requirements 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.
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Salary range: Rs 100000000 - Rs 200000000 (ie INR 100-200 LPA)
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.
Requirements 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.
#J-18808-Ljbffr