Role: Senior Azure AI Document Intelligence Engineer
Location: Edina, MN/ Chicago, IL/ Irving, TX
Type: Contract
Job Description:
Senior Azure AI Document Intelligence Engineer design, train, evaluate, and continuously improve the document-processing models used for supply-chain contracts and invoices.
This engineer will own extraction quality and be directly accountable for achieving the project’s 98–99% accuracy objectives.
Key Responsibilities
· Analyze invoice and contract variations across suppliers, countries, languages, formats, and scan-quality levels.
· Define the document taxonomy and extraction schema for:
· Invoice headers
· Contract metadata
· Purchase-order references
· Supplier and customer information
· Dates, currencies, taxes, discounts, and totals
· Payment terms, renewal dates, obligations, and termination clauses
· Tables and invoice line items
· Evaluate Azure AI Document Intelligence prebuilt invoice, layout, custom neural, custom template, classification, and composed-model capabilities.
· Build and train custom document classifiers and extraction models.
· Prepare, label, clean, balance, and version training and evaluation datasets.
· Implement document preprocessing for rotation, skew, noise, resolution, page separation, and scan-quality issues.
· Develop confidence-scoring and validation strategies using field-level and OCR-level confidence signals.
· Implement deterministic validation rules, including:
o Subtotal, tax, and total reconciliation
o Currency and date validation
o Purchase-order and supplier matching
o Duplicate-document detection
o Required-field and cross-field consistency checks
· Establish confidence thresholds and human-review rules for uncertain extractions.
· Perform error analysis by document type, supplier, field, language, and scan quality.
· Create automated evaluation pipelines reporting exact-match accuracy, precision, recall, F1 score, false-positive rates, and false-negative rates.
· Monitor model drift and retrain models when document formats or business requirements change.
· Document model versions, training data, experiments, limitations, and release decisions.
· Collaborate with supply-chain, procurement, accounts-payable, legal, and engineering teams.
Required Qualifications
· Bachelor’s or master’s degree in Computer Science, Data Science, Artificial Intelligence, Engineering, or a related discipline.
· 4+ years of experience in machine learning, OCR, computer vision, NLP, or intelligent document processing.
· Hands-on experience with Azure AI Document Intelligence or a comparable enterprise document-processing platform.
· Strong Python development skills.
· Experience with REST APIs, JSON, Azure SDKs, and asynchronous processing.
· Experience training and evaluating document classification and field-extraction models.
· Strong understanding of accuracy, precision, recall, F1 score, confidence calibration, and test-set design.
· Experience extracting complex tables and variable-length line items.
· Experience creating labeled datasets and maintaining data-quality standards.
· Familiarity with invoices, purchase orders, contracts, or supply-chain documents.
· Strong analytical, debugging, and technical-documentation skills.
Preferred Qualifications
· Experience with Azure Machine Learning, MLflow, Azure AI Search, or Azure OpenAI.
· Experience with multilingual documents.
· Familiarity with contract clause extraction and legal-document processing.
· Knowledge of ERP or procurement systems such as SAP, Oracle, Dynamics 365, Coupa, or Ariba.
· Experience implementing active learning, model-drift monitoring, and human-in-the-loop workflows.
· Knowledge of data privacy and document-retention requirements.
· Success Measures
o ≥99% exact-match accuracy for approved critical fields.
o ≥98% exact-match accuracy across all agreed fields.
· Accuracy maintained across suppliers, formats, and document-quality categories.
· Measurable reduction in manual corrections and review time.
· Documented improvement process for fields that miss their thresholds.
· No production model deployed without passing the approved evaluation suite.
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