Head of AI Factory

Managing the recruitment of a Head of AI Factory means first and foremost having a thorough understanding of the role. It is summarised here in very general terms and must be adapted to your specific context.

The Head of AI Factory is responsible for the industrial delivery of AI-based products and solutions, working closely with product and business teams. This role focuses on developing, industrializing, and deploying AI models at scale, ensuring their performance, reliability, and alignment with technical and business requirements.

Core Responsibilities

a. Industrialization and Delivery of AI Products

Development and Deployment

Oversee the manufacturing of AI models according to specifications defined by product and business teams.
Industrialize models (via MLOps) for scalable, robust, and reproducible deployment in production.
Ensure the quality, performance, and maintainability of delivered AI solutions.

b. Technical Team Management

Operational Leadership

Lead a multidisciplinary team (Data Engineers, MLEngineers, DevOps) dedicated to delivering AI products.
Organize work in agile mode (sprints, code reviews, testing) to meet deadlines and budgets.

Skill Development

Train teams in best practices (MLOps, automated testing, monitoring).
Keep up with technological advancements in industrialization tools (e.g., MLflow, Kubeflow, Docker).

c. Collaboration with Product and Business Teams

Alignment with Requirements

Work closely with Product Owners and business teams to understand functional and technical requirements.
Translate these requirements into technical specifications for development teams.

Integration and Support

Collaborate with IT/DevOps teams to integrate AI models into existing systems.
Provide technical support to business teams during the deployment phase.

d. Quality Assurance and Compliance

Testing and Validation

Implement automated testing processes (unit, integration, performance) to validate models before deployment.
Ensure compliance of solutions with security standards, regulations (GDPR, AI Act), and internal policies.

Monitoring and Maintenance

Oversee monitoring of models in production (performance, drift, bias).
Implement mechanisms for corrective and evolutionary maintenance.

e. Optimization of Delivery Processes

Continuous Improvement

Identify and resolve bottlenecks in delivery processes.
Automate data and model pipelines to accelerate deployment cycles.

Operational Reporting

Regularly report on progress, risks, and deliverables to stakeholders (CDAO, Product Owners, business teams).

Key Skills

a. Technical

AI Industrialization: Mastery of MLOps best practices (CI/CD, versioning, monitoring, scalability).
Experience with industrialization tools (MLflow, Kubeflow, Airflow, Docker, Kubernetes).
Software Development: Knowledge of programming languages (Python, SQL) and frameworks (TensorFlow, PyTorch, scikit-learn).
Experience in system integration and collaboration with DevOps teams.

b. Project Management and Leadership

Agile Management: Ability to manage projects in agile mode (Scrum, Kanban) with technical teams.
Operational Leadership: Aptitude to motivate and organize technical teams to meet delivery goals.

c. Collaboration and Communication

Team Spirit: Ability to work closely with product, business, and IT teams.
Clear Communication: Ease in explaining technical concepts to non-technical stakeholders.

Concrete Achievements Examples

Deployment of a Recommendation Model: Industrialized and deployed a personalized recommendation model for an e-commerce site, reducing delivery time from 4 weeks to 2 weeks.
Automation of an MLOps Pipeline: Set up a CI/CD pipeline for AI models, reducing deployment errors by 30%.
Monitoring of Models in Production: Deployed monitoring tools to detect performance drift in models, improving reliability by 25%.