Head of AI

Recruiting a Head of AI requires a thorough understanding of the role. The following is a very general summary, which should be adapted to your specific context.
(Reporting to the Chief Data Officer or Chief Data Scientist)

The Head of AI is the scientific and technical leader specialized in advanced AI technologies (deep learning, NLP, computer vision, generative AI) for analyzing and processing structured and unstructured data, particularly images, texts, sounds, document corpora, videos, and multimodal data. Their primary role is to drive the development and industrialization of sophisticated AI algorithms to extract insights, automate processes, and create value from these complex data types. They oversee a team of AI experts, ML engineers, and GenAI researchers, while operating in close agile (matrix) collaboration with product teams who have authority over the backlogs of their team members.

Responsibilities and Missions

1. Define Advanced AI Strategy and Roadmap

Develop a specialized vision for processing multimodal data with AI.
Prioritize advanced use cases (image recognition, text processing, sound analysis, etc.).
Define technical standards for models processing unstructured data.
Collaborate with management to align initiatives with business objectives.

2. Develop Cutting-Edge AI Solutions for Complex Data

Oversee development of specialized models (computer vision, advanced NLP, signal processing, multimodal models).
Ensure technical excellence of solutions and their industrialization via MLOps.
Collaborate with product teams to integrate solutions into business products.
Implement continuous performance evaluation mechanisms for models.

3. Collaborate in Agile/Matrix Mode with Product Teams

Work closely with Product Owners who have authority over the backlogs of AI experts.
Provide technical expertise to refine product requirements.
Participate in agile ceremonies to align algorithmic developments.
Ensure technical constraints are considered in planning.

4. Ensure Ethics and Compliance of AI Solutions

Define strict ethical standards for advanced AI models.
Ensure compliance with regulations (GDPR, AI laws).
Assess and mitigate specific risks (bias, security, explainability).
Document processes to ensure traceability and compliance.

5. Manage and Grow the AI Expert Team

Recruit specialists in deep learning, NLP, and computer vision.
Structure the team by domains of technical expertise.
Mentor members on the latest AI advancements.
Promote a culture of innovation in advanced AI.

6. Innovate and Anticipate AI Evolutions

Monitor advancements in deep learning and generative AI.
Lead PoCs on latest technologies (LLMs, diffusion models).
Collaborate with academic and industry partners.
Represent the company at specialized AI events.

Examples of Concrete Achievements

Developed a computer vision model for medical image analysis, improving pathology detection by 35%.
Implemented an advanced NLP system for information extraction from document corpora, reducing search times by 60%.
Created a sound processing platform for automatic voice recording analysis, improving transcription quality by 40%.
Optimized a multimodal model combining text and images for product recommendations, increasing conversions by 25%.
Deployed a report generation system from structured and unstructured data, reducing production time by 50%.