Azure Document AI and Data Engineer

Unicorn Technologies LLC; Atlanta (GA); On-site; USD 150,000 - 190,000; Full time

Job summary

Unicorn Technologies LLC is seeking a senior Document AI and Data Engineer to lead OCR, document classification, and metadata extraction initiatives. You will design schemas, manage pipelines for validation, provenance, and retraining, and work across digital and degraded source materials.

Deep experience with Azure AI Document Intelligence, Python, SQL, and data-quality tooling is required to build robust end-to-end processing and evaluation workflows while maintaining model governance and

Qualifications

• Eight or more years in data engineering, document AI, OCR, or related data-processing disciplines.
• Experience with Azure AI Document Intelligence Studio, APIs, SDKs, and custom models.
• Experience with PDF analysis, text-layer detection, image preprocessing, and degraded-scan handling.
• Strong Python, SQL, and Azure data-engineering capabilities.
• Knowledge of metadata schemas and structured outputs.

Responsibilities

• Configure OCR, layout-analysis, classification, and extraction models.
• Define schemas, labels, validation rules, and structured outputs.
• Implement file-integrity, text-layer, processability, and duplicate checks.
• Process digital documents, scanned files, images, and degraded source materials.
• Retain confidence values, page references, text spans, and source regions.
• Build normalization, transformation, and data-quality pipelines.
• Create training, validation, test, and golden datasets.
• Evaluate model accuracy, corrections, missing fields, and false predictions.
• Manage model versioning, promotion, rollback, and retraining.
• Preserve source, model, dataset, and reviewer provenance.

Skills

Data engineering

Document AI

OCR

ML pipelines

Python

SQL

Azure services

Model evaluation

Data quality pipelines

Provenance tracking

Tools

Azure AI Document Intelligence

Azure Blob Storage

Azure SQL

Service Bus

Functions

JSON

REST APIs

Job description

Seeking a senior Document AI and Data Engineer to implement OCR, document classification, metadata extraction, confidence scoring, structured-data, model-evaluation, and controlled retraining pipelines.

Responsibilities

• Configure OCR, layout-analysis, classification, and extraction models.
• Define schemas, labels, validation rules, and structured outputs.
• Implement file-integrity, text-layer, processability, and duplicate checks.
• Process digital documents, scanned files, images, and degraded source materials.
• Retain confidence values, page references, text spans, and source regions.
• Build normalization, transformation, and data-quality pipelines.
• Create training, validation, test, and golden datasets.
• Evaluate model accuracy, corrections, missing fields, and false predictions.
• Manage model versioning, promotion, rollback, and retraining.
• Preserve source, model, dataset, and reviewer provenance.

Required skills

• Eight or more years in data engineering, document AI, OCR, machine learning, or related data-processing disciplines
• Azure AI Document Intelligence Studio, APIs, SDKs, and custom models
• Read, Layout, classification, template, and neural extraction models
• PDF analysis, text-layer detection, image preprocessing, and degraded-scan handling
• Python, JSON, SQL, Blob Storage, Service Bus, Functions, and Azure SQL
• Document labeling, schema design, field validation, and source-region mapping
• Confidence, false-positive, false-negative, and missing-field analysis
• Golden datasets, model registries, versioning, regression testing, promotion, and rollback
• Provenance, model lineage, dataset lineage, and reviewer-correction tracking
• Production pipeline monitoring, exception handling, and performance tuning
• OCR and document-processing pipeline
• Classification and metadata-extraction models
• Metadata schemas, validation rules, and structured outputs
• Golden training and evaluation datasets
• Model registry and controlled retraining framework

Preferred qualifications

• Strong Python, SQL, Azure data-engineering, and machine-learning pipeline experience
• Experience with large or historically diverse document collections