Data Architect IA

Senior Data & AI Architect to lead the technical design and implementation of data labeling and knowledge management solutions as part of a major transformation project. The consultant will offer deep expertise in data architecture, data labeling, and large-scale data integration, particularly with unstructured data. The consultant has experience with these activities in Microsoft technical environments. The role requires a strategic thinker who can identify cutting-edge technical solutions that meet functional needs, ensuring that the processes feeding data labeling are scalable, secure, and compliant with regulations. The augmented data produced will be made available in a format that can be consumed by third-party tools such as search tools like SharePoint or AI (specifically Microsoft Copilot).

Key Responsibilities:

1. Solution Architecture: Design and oversee the implementation of a scalable data labeling and AI architecture, integrating both structured and unstructured data sources (100+ TB).
2. Data Governance and Compliance: Ensure that the data architecture aligns with our client data governance policies and complies with GDPR, AI Act, and other relevant regulations. Design secure data flows to handle sensitive information.
3. Integration with client Systems: Lead the integration of AI models with client existing systems, including SharePoint Online and specific knowledge bases. Ensure smooth data flow and access management.
4. Global Leadership: Collaborate closely with client's global teams to develop, promote, and deploy data architecture standards that enhance CLIENT's data assets, potentially enabling their reuse on an international scale.
5. Stakeholder Engagement: Work with business units (Audit, Tax, Advisory) to identify use cases, validate solutions, and ensure AI tools meet operational needs.
6. Team Leadership: Collaborate with AI/ML engineers, data engineers, and DevOps teams to implement and scale AI solutions. Act as a mentor and technical leader, ensuring the team adheres to best practices for AI model development and deployment.
7. AI Model Development for Data Labeling: Lead the selection, design, and implementation of AI/ML models, including NLU and LLM models tailored to CLIENT's needs for data labeling, search, and retrieval.
8. Platform Selection & MLOps: Evaluate AI platforms (e.g., Azure AI Studio, private cloud environments) and implement MLOps pipelines to ensure continuous development, version management, and monitoring of models.
9. Performance Monitoring & Optimization: Define KPIs and set up performance monitoring systems to ensure AI models' accuracy and reliability. Continuously optimize the architecture to improve precision, latency, and scalability.

Qualifications:

1. Education: Master's degree in Computer Science, Data Science, AI, or a related field.
2. Experience:
   • Minimum of 5 years of experience in enterprise data architecture roles.
   • Proven experience in designing and implementing AI/ML solutions in enterprise environments with large datasets.
   • Practical experience with data labeling techniques (semi-supervised, active learning) and tools such as Snorkel, Labelbox, or Prodigy.
   • Experience with LLMs such as OpenAI, Llama, Mistral, or similar model.
   • Experience with RAG systems, including open-source or enterprise-level solutions like GraphRAG.
   • Strong knowledge of data governance, GDPR compliance, and AI ethics.
   • Familiarity with MLOps pipelines and platforms like Azure AI Studio.
3. Technical Skills:
   • Experience with the Microsoft suite (Office 365, SharePoint, Copilot):
     • Ability to master data access management and compliance.
     • Expertise in the security and management of sensitive data.
     • Deep knowledge of SharePoint's metadata management features (Taxonomy & Live Metadata).
   • Expertise in Microsoft Cloud platforms, particularly:
     • Azure AI Studio: Mastery of Microsoft's AI tools to design, deploy, and integrate large-scale AI models.
     • AI Models: Proven experience integrating and using NLU and GPT models for data labeling and advanced search tasks.
     • Metadata Integration: Skill in configuring and integrating metadata within the Microsoft environment (SharePoint and Copilot) to optimize business processes and improve productivity.
4. Leadership Skills:
   • Proven technical leadership skills.
   • Excellent communication and stakeholder management skills.
   • Ability to translate functional requirements into technical solutions.

Ubicaciones: Paris, Estado remoto, Híbrido