AI Security Engineer

Thales Group; Zaventem; On-site; EUR 90,000 - 130,000; Full time

Job summary

Thales Group in Belgium is seeking an AI Security Engineer to design, develop and deploy secure AI systems for client engagements and internal needs. The role blends software engineering with applied AI, focusing on secure LLM integrations, RAG, agents and AI-driven automations, while embedding security best practices from design to production.

You will work on scoping workshops with clients, build and industrialize AI tools, support production deployments and contribute to internal standards

Responsibilities

• Deliver on client projects that require software engineering and the secure integration of AI components, with a level of autonomy adapted to the profile and to the engagement context.
• Take part in scoping workshops with clients: understanding the business need, translating it into a technical architecture, identifying security and compliance constraints.
• Design, prototype and develop the solutions delivered to clients: LLM integrations, RAG, agents, AI-driven automations, within the framework set by the AI Security Advisor.
• Support the client through to production: industrialization, secure deployment pipelines, knowledge transfer to the client's internal teams.
• Contribute to pre-sales activities: demos, PoCs, technical estimates, technical sections of RFP responses, alongside the sales teams and the AI Security Advisor.
• Represent the organization in front of clients with a strong level of communication, pedagogy and consulting posture.
• Capitalize after each engagement: lessons learned, reusable components, contributions to internal frameworks and standards.

Job description

Les entreprises et les gouvernements comptent sur Thales pour apporter de la confiance aux milliards d'interactions numériques qu'ils établissent avec les utilisateurs. L'Activité Mondiale Identité & Sécurité Numériques (DIS) fournit des technologies et services (des logiciels sécurisés en passant par la biométrie ou encore le cryptage), qui permettent aux entreprises ainsi qu'aux gouvernements de vérifier des identités, et de protéger les données afin qu'elles restent sûres.

AI Security Engineer

The AI Security Engineer is a technical and consulting role focused on the design, development and production deployment of AI systems, both for client engagements and to support the organization's internal needs. The role complements the AI Security Advisor: where the latter drives strategy, governance and advisory, the AI Security Engineer focuses on execution. They build, ship and maintain the solutions, with clients and internally, embedding security best practices from design onward.

The role combines a strong appetite for software engineering with curiosity for applied AI (LLMs, agents, RAG, MLOps) and the associated security challenges. A significant share of the activity is dedicated to client engagements: requirements gathering, design, prototyping, development, support to production rollout, and knowledge transfer. In parallel, the AI Security Engineer contributes to the organization's strategic internal initiatives (AI-enabled business tools, agentic automations for security, transverse RAG platforms), which also serve as a learning ground and as a basis for capitalizing on client engagements.

Direct report: AI Security Advisor / Senior Management

Tasks and Responsibilities

Client engagements and consulting

• Deliver on client projects that require software engineering and the secure integration of AI components, with a level of autonomy adapted to the profile and to the engagement context.
• Take part in scoping workshops with clients: understanding the business need, translating it into a technical architecture, identifying security and compliance constraints.
• Design, prototype and develop the solutions delivered to clients: LLM integrations, RAG, agents, AI-driven automations, within the framework set by the AI Security Advisor.
• Support the client through to production: industrialization, secure deployment pipelines, knowledge transfer to the client's internal teams.
• Contribute to pre-sales activities: demos, PoCs, technical estimates, technical sections of RFP responses, alongside the sales teams and the AI Security Advisor.
• Represent the organization in front of clients with a strong level of communication, pedagogy and consulting posture.
• Capitalize after each engagement: lessons learned, reusable components, contributions to internal frameworks and standards.

Development and industrialization of internal AI tools

• AI-enabled business tools : actively contribute to the development and evolution of internal solutions (for example in CRM, due diligence or decision support domains), embedding software quality and security best practices.
• Document processing pipelines : contribute to the development and maintenance of automated document processing tools, ensuring pipeline robustness and the protection of the data being handled.
• Agentic automations for security : develop agentic components for cybersecurity operations (notably SOC use cases), in close collaboration with the Cyber Defense teams and the AI Security Advisor.
• Design and develop internal tools based on RAG, agents and AI-driven automations to support the needs of IT, HR and other departments.
• Industrialize PoCs and PoVs produced by the AI team into robust, maintainable solutions.
• Document, test and maintain the delivered solutions over time.

Embedding security in AI development

• Apply secure coding and application security best practices in the code produced, both internally and for clients.
• Secure AI deployment pipelines: CI/CD, secrets management, dependency control, API and cloud integrations.
• Implement defense mechanisms suited to AI systems: guardrails, input and output filtering, monitoring, control over the tools accessible to agents.
• Contribute, with the AI Security Advisor, to the operational implementation of reference frameworks (OWASP LLM Top 10, MITRE ATLAS, NIST AI RMF) within projects.
• Take part in threat modeling and code review activities on AI components, in collaboration with the AI Security Advisor.

Watch and capitalization

• Keep up to date on tools, frameworks and best practices for AI development and the associated application security.
• Develop reusable libraries, scripts and components to accelerate future projects.
• Contribute to internal knowledge sharing: lessons learned, demos, documentation.
• Capitalize on project insights to feed the standards and frameworks driven by the AI Security Advisor.

Collaboration and communication

• Work closely with the AI Security Advisor to ensure alignment between strategy and execution