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Document AI Engineer

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Hybrid

Document AI Engineer

MarLabs

Document AI Engineer

Skills

PythonMachine LearningAIAWSNLP / Natural Language ProcessingAzure

2 months ago

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Important information


Contract type:

Permanent

Location:

Karnataka, India

Work mode:

Hybrid

Published on:

24 September 2026

What they need


Marlabs, a global AI and Digital Solutions Consulting firm, delivers intelligent solutions across AI, data, analytics, and product engineering. Since 2000, we have partnered with some of the largest healthcare, life sciences, financial services, and government organizations worldwide. As we continue to expand our global footprint, we have an exciting opportunity for a highly skilled Document AI Engineer to join our innovative and dynamic team.

Document AI Engineer | About You

As a Document AI Engineer, you will develop intelligent document processing solutions that transform unstructured content into structured, business-ready information. You will leverage OCR, computer vision, machine learning, NLP, and LLM technologies to automate classification, extraction, validation, and quality scoring processes.

Document AI Engineer | Day-to-Day

  • Build document classification and extraction models using OCR and AI technologies.
  • Develop document processing pipelines for ingestion, validation, enrichment, and workflow routing.
  • Implement confidence scoring, completeness analysis, and document quality validation.
  • Configure source-evidence linking, citation tracing, and lineage capabilities.
  • Optimize extraction accuracy and model performance using training and validation datasets.
  • Collaborate with AI, integration, and business teams to enhance document automation workflows.

Document AI Engineer | Skills & Experience

  • 5+ years of experience with OCR, Document AI, Intelligent Document Processing, or NLP solutions.
  • Experience with Azure Document Intelligence, ABBYY, AWS Textract, Google Document AI, or similar platforms.
  • Strong Python programming experience.
  • Experience with LLMs, embeddings, and AI-assisted document processing.
  • Knowledge of machine learning pipelines, model validation, and performance tuning.

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