Company Description
Inetum is a European leader in digital services. Inetum’s team of 28,000 consultants and specialists strive every day to make a digital impact for businesses, public sector entities and society. Inetum’s solutions aim at contributing to its clients’ performance and innovation as well as the common good.
Present in 19 countries with a dense network of sites, Inetum partners with major software publishers to meet the challenges of digital transformation with proximity and flexibility.
Driven by its ambition for growth and scale, Inetum generated sales of 2.5 billion euros in 2023.
Job Description
We are looking for a highly skilled professional to lead the industrialisation of AI use cases, managing their lifecycle in production and ensuring robustness, traceability, and compliance with the highest standards of quality and security.
You will work closely with data science, data engineering, cybersecurity, compliance, and IT operations teams to deliver reliable, scalable, and secure AI services.
Responsibilities:
Industrialisation and Deployment
- Analyse, monitor, and optimise ML models, tracking their performance.
- Design and implement CI/CD pipelines for ML models and data flows.
- Containerise and deploy models via APIs, batch processes, and streaming.
- Manage model versioning and traceability.
- Ensure continuous improvement and adaptation of AI use cases and ML models.
Operations and Observability
- Set up monitoring and alerting for model performance.
- Establish incident response protocols in collaboration with IT.
- Maintain dashboards and automated reports on model health.
Quality Assurance
- Implement validation frameworks for data and models (e.g., Great Expectations, unit tests, stress tests), in collaboration with Group Governance.
- Contribute to documentation and apply technical best practices.
- Ensure full traceability, auditability, and explainability of models in production.
Security and Compliance
- Apply security-by-design principles: IAM policies, encryption, secure APIs.
- Align with regulatory standards (e.g., GDPR, DORA, AI Act).
- Collaborate with cybersecurity and risk teams to validate and monitor AI implementation.
Qualifications
Required
- Minimum of 5 years’ experience in MLOps, data engineering, or DevOps with a focus on ML/DL/LLM/AI agents in production environments.
- Strong proficiency in Python.
- Hands-on experience with CI/CD tools such as GitLab, Docker, Kubernetes, Jenkins.
- Solid understanding of ML, DL, and LLM models.
- Experience with ML lifecycle tools such as MLflow or DVC.
- Good understanding of model lifecycle, data traceability, and governance frameworks.
- Experience with on-premise and hybrid infrastructures.
- Excellent communication skills and ability to collaborate with remote teams.
- Proactive mindset, technical rigour, and engineering mentality.
- Willingness to learn, document, and standardise best practices.
- Innovative mindset and ability to drive change.
- English proficiency at C1 level.
Desirable
- Knowledge of Bash / YAML / SQL.
- Experience in developing and deploying agentic tools.
- Experience working with security teams or implementing IAM policies and data protection.
- French proficiency at B2 level.
