Design secure reference architectures and patterns for AI platforms and AI applications
Consult product, data science, and platform teams on secure AI design and implementation
Secure AI systems against data poisoning, model theft, adversarial attacks, and other AI‑specific threats
Define and implement measures for model integrity, data privacy, bias mitigation, and robustness
Perform threat modeling and risk reviews tailored to AI workflows and pipelines
Implement and manage data security measures
Ensure strong authentication and authorization (e.g. OAUTH2, RBAC, PIM) across AI services and platforms
Design and implement secure architectures for cloud platforms (i.e. Azure, AWS, GCP)
Guide secure use of containers, orchestration, and Infrastructure as Code (e.g. Terraform, PowerShell) for AI workloads
Integrate requirements from ISO/IEC 27001, SOX, HIPAA, PCI DSS into AI security concepts
Ensure alignment with ethical AI principles and evolving AI regulation, including the EU AI Act
Define and review security concepts for AI platforms; conduct security reviews and architecture assessments
Support incident response related to AI systems, advising on detection, containment, and remediation
Technical leadership and collaboration with international security teams, fostering collaboration across diverse, cross-functional groups
Act as a key link between central security, regulatory functions, and the Digital Technology Accelerator
Contribute actively to the ZEISS‑wide security community, sharing best practices in AI security

Your Profile:

A Master's degree in Computer Science, Software Engineering, Electrical Engineering, Mechanical Engineering, Mathematics, or Physics
Minimum of 7+ years of professional experience in roles such as data modeling, algorithm development, data science, or similar fields
Proven track record in designing and implementing secure architectures for AI platforms and AI-driven use cases
Cloud Security: Certifications or training in cloud platforms (AWS, GCP, Azure preferred) and expertise in cross-provider cloud technologies (e.g., Microsoft Azure, Google GCP, Amazon AWS, Alibaba)
Strong understanding of software architectures, design patterns, and abstract thinking, with the ability to map capabilities to technologies effectively
Knowledge of HSM, certificate infrastructures, key vaults, TLS protocols, and authentication methods like OAUTH2
Expertise in securing AI platforms, addressing risks like data poisoning and model theft, and ensuring compliance with ethical AI principles, regulatory standards, and the EU AI Act
Experience with regulatory frameworks (ISO/IEC 27001, SOX, HIPAA, PCI DSS, EU AI Act) and integrating compliance requirements into AI security strategies
Proficiency in Python, managing code repositories, containerizing artifacts, and implementing "Infrastructure as Code" using tools like Terraform and PowerShell
Proven ability to give technical guidance to international teams, fostering collaboration and driving security initiatives across diverse, cross-functional groups

Preferred Certifications

AI Security Certifications (e.g., AI-specific cybersecurity certifications)
Advanced Cloud Security Certifications (e.g., AWS Certified Security Specialty, Microsoft Certified: Azure Security Engineer Associate)

Your ZEISS Recruiting Team:

Markus Repp

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