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:

• Solution Architecture: Design and oversee the implementation of a scalable data labeling and AI architecture, integrating both structured and unstructured data sources (100+ TB).
• 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.
• 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.
• 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.
• Stakeholder Engagement: Work with business units (Audit, Tax, Advisory) to identify use cases, validate solutions, and ensure AI tools meet operational needs.
• 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.
• 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.
• 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.
• 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:

• Education: Master’s degree in Computer Science, Data Science, AI, or a related field.
• 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.
• 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.
• Leadership Skills:
• Proven technical leadership skills.
• Excellent communication and stakeholder management skills.
• Ability to translate functional requirements into technical solutions.