AI Engineer

Weekday 1; Chicago (IL); On-site; USD 140,000 - 190,000; Full time

Job summary

Weekday is seeking an experienced AI Engineer to design and build the intelligence layer across a document-to-return workflow. The role focuses on production-grade AI that transforms complex financial and tax documents into structured data and supports professionals in identifying missing information, inconsistencies and potential errors.

This hands-on role emphasizes document OCR, LLM-powered understanding, agentic systems, evaluation, and human-in-the-loop workflows to ensure accuracy,

Qualifications

• 3+ years of experience building production AI systems.
• Hands-on experience with document OCR, understanding, parsing and structured data extraction.
• Experience with PDFs, forms, scanned documents, financial documents, or other unstructured data.
• Strong knowledge of LLM-based document processing and AI techniques for extraction and reasoning.
• Experience designing evaluation frameworks for AI or ML systems.
• Knowledge of ground-truth labeling, scoring methodologies, benchmarking and statistics.
• Experience with agentic AI systems and human-in-the-loop workflows.
• Strong observability, reproducibility, model monitoring and production reliability.
• Proficiency in Python and building scalable production systems.
• Bias toward shipping, experimentation, measurement and continuous improvement.

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 present findings clearly for professional review.
• Design human-in-the-loop workflows with confidence signals, source citations, review controls and correction mechanisms.
• Build scalable evaluation frameworks for 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 where AI can add value.
• Collaborate with engineering and domain experts to translate 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.

Skills

Python

Observability

Production AI

Experimentation

Problem solving

Tools

OCR

PDF processing

LLM tooling

Job description

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 experiencedAI Engineerto 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 ondocument 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 fordocument 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.
• DevelopLLM-powered document understandingand 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 scalableevaluation frameworksfor 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 experiencebuilding production AI, machine learning, or intelligent automation systems.
• Strong hands-on experience withdocument OCR, document understanding, parsing, and structured data extraction.
• Experience working with PDFs, forms, scanned documents, financial documents, or other complex unstructured data.
• Strong understanding ofLLM-based document processingand modern AI techniques for extraction and reasoning.
• Experience designing and implementingevaluation frameworksfor AI or machine-learning systems.
• Strong knowledge of evaluation datasets, ground-truth labeling, scoring methodologies, benchmarking, and statistical analysis.
• Experience building or working withagentic AI systemsand human-in-the-loop workflows.
• Strong understanding of observability, reproducibility, model monitoring, and production AI reliability.
• Proficiency inPythonand 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 aretraceable, 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 asmall, high-ownership environmentwhere engineering and product responsibilities are closely connected.