GEN AI Expert/ Developer

N Consulting; Paris; On-site; EUR 40,000 - 60,000; Full time

Job summary

A technology consulting firm based in France is looking for a candidate to develop and implement AI-driven document extraction solutions using GenAI and Microsoft Azure services. The role demands expertise in data workflows that convert unstructured content into structured outputs, ensuring security and compliance in AI applications. Ideal candidates have 1-2 years of hands-on project experience in this domain and a solid understanding of prompt engineering techniques.

Qualifications

• 1-2 years of real-world project experience with GenAI technologies.
• Understanding of Azure Cognitive Services.
• Familiarity with unstructured document processing.

Responsibilities

• Develop and implement GenAI based document extraction pipelines.
• Build intelligent data extraction workflows.
• Design, test, and optimize prompt engineering techniques.
• Collaborate with teams to map document formats and data requirements.
• Implement quality, security, and compliance best practices.
• Monitor model performance and analyze extraction errors.

Skills

GenAI document extraction

Microsoft OpenAI models

Azure AI Document Intelligence

prompt engineering

data validation

error reduction strategies

Job description

• Develop and implement GenAI based document extraction pipelines using Microsoft OpenAI models, Azure AI Document Intelligence (Form Recognizer), and related Azure Cognitive Services.
• Build intelligent data extraction workflows that interpret unstructured documents (orders, PDFs, emails, handwritten notes) and convert them into structured, validated outputs.
• Design, test, and optimize prompt engineering, grounding techniques, and model orchestration to improve accuracy and reliability of extraction results.
• Collaborate with product and engineering teams to map document formats, business rules, and data requirements into scalable AI extraction solutions.
• Implement end to end quality, security, and compliance best practices for enterprise AI, including data privacy handling, model evaluation, and error reduction strategies.
• Continuously monitor model performance, analyze extraction errors, and iterate through fine tuning, pattern recognition, and workflow optimization to improve system accuracy.
• Experience : 1 - 2 years with real world project experience and not just POCs