AI/ML Data Engineer

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.

We are looking for an AI/ML Data Engineer to join a team focused on transforming data and incident response processes into a robust, secure, and production-ready AI system. This role combines data engineering, automation, and cybersecurity, with a strong impact in a production environment.

Soft Skills

- High level of autonomy and critical thinking
- Ability to work in complex, production-oriented environments
- Strong sense of ownership and attention to detail , especially in security contexts
- Good communication and collaboration skills with both technical and business teams
- Continuous improvement mindset and focus on operational efficiency

Responsibilities

Develop and operate data pipelines (ingestion, normalization, and enrichment) for security events (logs, alerts, and tickets); Implement end-to-end integrations with SOC and ITSM tools , ensuring data consistency and quality; Support the incident lifecycle (triage, investigation, and response) through AI-assisted models and automated playbooks; Optimize response processes by reducing operational noise and improving incident prioritization; Implement and evolve automation and orchestration workflows (SOAR); Ensure MLOps/AIOps practices , including versioning, performance monitoring, and drift detection; Guarantee governance, security, and compliance in the use of data and AI models; Mitigate risks associated with AI usage (e.g., prompt injection, data leakage, incorrect outputs); Collaborate with vendors and internal teams on the integration and operation of AI solutions; Contribute to technical and strategic decision-making, including risk and ROI analysis

Requirements

Strong experience in Python (data engineering, automation, and integration); Solid knowledge of SQL / PostgreSQL; Experience with data pipelines and log/event processing; Knowledge of ML/Deep Learning frameworks (e.g., PyTorch, TensorFlow); Familiarity with versioning (Git), logging, and observability practices; Knowledge of access control and authentication (SSO, SAML, LDAP); Experience or ability to work with data and ML infrastructure in production environments

Nice to have

Experience with SIEM, SOAR, and EDR; Experience with high-availability environments and infrastructure (including GPU); Knowledge of MLOps/AIOps and AI governance