Chief Technology Officer

Recruiting a Chief Technology Officer (CTO) requires a thorough understanding of the position. The summary below is general and should be adapted to your specific business context.

The CTO reports directly to the CEO or Executive Committee. In a context of data and AI transformation, their role is to rethink IT as a strategic lever: designing a scalable data/AI platform on public cloud, modernizing infrastructures, industrializing data/AI processes, and unifying technical and business teams around a shared vision. Their goal is to accelerate the adoption of AI and advanced analytics, while ensuring the security, performance, and scalability of systems.

Responsibilities and Missions

1. Design and Deploy a Data/AI Platform on the Cloud

Lead the creation of a unified data/AI platform (AWS, Azure, or GCP) to centralize, process, and exploit data at scale.
Architect cloud-native solutions (data lake, data warehouse, ETL/ELT pipelines) for analytics and AI workloads.
Integrate orchestration tools (Airflow, Databricks) and governance tools (Collibra, Alation) to guarantee quality, traceability, and compliance.
Automate data ingestion, transformation, and storage to improve speed and reliability.

2. Modernize IT Infrastructure for Data and AI

Migrate legacy systems to the cloud (lift-and-shift, refactoring) to reduce costs and improve flexibility.
Adopt microservices and serverless architectures to support AI models and data-intensive applications.
Optimize infrastructure performance (latency, availability) for demanding workloads (model training, real-time processing).

3. Industrialize Data/AI Processes to Accelerate Innovation

Implement automated data pipelines (Kafka, Spark) to fuel AI models and analytics dashboards.
Deploy collaborative environments (Jupyter, MLflow) to support data science and MLOps.
Standardize deployment workflows (CI/CD for AI models) to reduce time-to-production.

4. Build an Agile, Data/AI-Focused IT Organization

Drive adoption of Agile and DevOps methodologies to shorten development cycles and foster cross-team collaboration.
Train IT teams in key data/AI skills (cloud, Python, MLOps) and promote company-wide data literacy.
Create Centers of Excellence (Data Lab, AI Factory) to consolidate expertise and spread best practices.

5. Ensure Security, Compliance, and Governance

Implement data protection measures (encryption, access control, anonymization) in line with GDPR and other regulations.
Collaborate with the CISO to secure cloud platforms and AI models against cyber threats.
Establish governance frameworks (data mesh, data fabric) to ensure traceability, quality, and ethical use of data.

6. Measure Impact and Continuously Improve

Define KPIs (data processing speed, adoption rates, AI ROI) to track efficiency and business value.
Collect and analyze feedback from business users and data scientists to refine platforms and workflows.
Optimize IT and cloud costs while maintaining high scalability and reliability.

Examples of Achievements

Built a data/AI platform on AWS, centralizing 10 TB of data and deploying 5 AI models in production within 12 months.
Migrated 80% of legacy systems to the cloud, reducing infrastructure costs by 25% and improving scalability.
Automated pipelines with Apache Airflow, reducing reporting delays by two-thirds.
Trained 300 employees on data/AI tools (Python, SQL, ML) and created a Data Lab to foster innovation.
Deployed a MLOps framework (MLflow, Kubernetes), cutting AI model deployment time by 50%.
Achieved ISO 27001 certification for the data platform, strengthening security and compliance.