Senior AI Security Engineer | Pharma

• Degree or equivalent practical experience in Cybersecurity, Computer Science, Artificial Intelligence, Software Engineering, or a related technical field.

• Proven experience in Security Architecture, Application Security, Cloud Security, Platform Security, Product Security, or Security Engineering.

• Practical exposure to securing AI, Machine Learning, or Generative AI solutions.

• Solid understanding of Machine Learning, Generative AI, Retrieval-Augmented Generation (RAG), and Agentic AI architectures, including associated security risks and data flows.

• Hands-on experience with threat modeling and security assessments.

• Knowledge of secure software development practices and security testing methodologies.

• Experience with Identity & Access Management (IAM), secrets management, cloud security controls, and development pipeline security.

• Ability to translate complex security concepts into practical guidance for technical and non-technical stakeholders.

• Strong collaboration and communication skills.

• Fluent professional English.

• Experience with AI security testing, red teaming, adversarial AI testing, or AI risk assessments.

• Exposure to AI engineering practices and MLOps environments.

• Experience working in regulated industries such as Pharma, Healthcare, Finance, or Life Sciences.

• Relevant cybersecurity certifications (CISSP, CCSP, CSSLP, AWS Security Specialty, Azure Security Engineer, or similar).

• Knowledge of privacy, compliance, and responsible AI frameworks.

• German and/or Spanish language skills.

• Experience securing cloud-native environments and enterprise AI platforms.

CONDITIONS 🌱:

• Salary package based on your profile.

• Ticket restaurant included in-office hours.

• Flexible compensation plan (free of income tax) where we provide you with medical insurance, public transport ticket and childcare check.

• Discounts in gym network WellHub.

• Training catalogue.

Our goal is that you are well in every way!

We are looking for a Senior AI Security Engineer to help secure the adoption of Artificial Intelligence across enterprise environments, contributing to the design, implementation, and governance of security controls for AI-powered solutions.

In this hands-on role, you will work closely with cybersecurity, architecture, engineering, and AI platform teams to translate AI security strategy into practical controls, reusable patterns, and implementation guidance for Machine Learning, Generative AI, RAG, and Agentic AI solutions deployed at enterprise scale.

• Translate AI security risks and strategic objectives into practical security controls, reusable architecture patterns, technical standards, and implementation guidelines.

• Conduct AI security assessments, architecture reviews, threat modeling exercises, and security testing activities for AI-enabled solutions.

• Review and support Machine Learning, Generative AI, Retrieval-Augmented Generation (RAG), and Agentic AI architectures from a security perspective.

• Collaborate with development and platform teams to integrate security controls into AI applications, cloud platforms, development pipelines, and AI engineering processes.

• Define security requirements for AI applications, models, data, agents, identities, integrations, monitoring, and incident response capabilities.

• Support the implementation of secure development practices across AI initiatives.

• Work closely with cybersecurity, enterprise architecture, AI platform, privacy, legal, quality, and business teams to establish sustainable AI security practices.

• Contribute to the evolution of enterprise AI security standards, frameworks, and best practices.

• Promote security-by-design principles throughout the AI solution lifecycle.

SCHEDULE 🕘:

• 08h-17h from Monday to Friday (flexible)

• Prefereably assistance 1 day/office; if it is not possible, full remote.

• AI solutions are delivered with security embedded from design through operation.

• Security requirements are translated into practical and scalable implementation patterns.

• AI risks are proactively identified, assessed, and mitigated.

• Development teams are empowered to build secure AI solutions through clear standards and guidance.

• Consistent AI security practices are established across enterprise platforms and business domains.