AI Security Architect

• 7+ years in security architecture or cloud security, designing and securing complex, multi-cloud environments
• Proven hands-on experience securing AI/ML and LLM-based systems, including data , model, and infrastructure layers, and implementing effective containment measures for security incidents
• Strong knowledge of AI-specific threats such as prompt injection, data leakage, model abuse, and agent/tool misuse
• Understanding of AI infrastructure components, including model hosting, vector databases, orchestration frameworks, and AI gateways
• Experience conducting security assessments, adversarial testing, threat modeling, or red-team style evaluations of AI systems
• Familiarity with AI security frameworks and standards, such as NIST AI RMF, OWASP Top 10 for LLM Applications, MITRE ATLAS, and ISO/IEC 42001
• Experience with threat modeling and designing security controls, guardrails, and isolation for complex workloads
• Solid cloud security background in GCP and/or AWS , including IAM, network isolation, containers, and logging
• Experience defining security telemetry and detection use cases and integrating logs into SIEM or real-time analytics platforms
• Ability to turn strategy into actionable engineering requirements
• Strong communication and stakeholder management skills, fluent English

About the role

In this role you will build the foundations of a secure AI program from the ground up, working at the intersection of security architecture, cloud engineering, and artificial intelligence. You will treat AI security as an engineering discipline — grounded in clear architecture, recognized frameworks, and well-defined controls — and translate it into concrete, actionable work that engineering teams can implement, test, and continuously improve. You will primarily work with AI and LLM-based workloads, including the newest models deployed in isolated cloud environments, defining how they are designed, deployed, monitored, and governed, with a strong focus on guardrails, threat models, isolation requirements, and logging and monitoring standards.

,[Build the foundations of the AI-based security program, including the roadmap, governance, and approval processes, Define how AI can be safely used for security testing, such as AI-assisted code scanning and vulnerability discovery, including validation of findings and their integration into the vulnerability management process, Define the AI security architecture and select and adapt the most suitable AI security frameworks, Design threat models for AI workloads and define security controls for AI environments, Design reference architectures for secure AI platforms deployed across AWS and GCP, Define secure operating principles, deployment standards, and isolation requirements for frontier models, Design guardrails, along with monitoring and logging requirements for AI systems, Translate architecture and standards into an implementation backlog for engineering teams and support its delivery] Requirements: Security, Cybersecurity, Cloud security, AI, AWS, GCP, NIST, OWASP, ISO, Python, Stakeholder management Additionally: Sport subscription, Training budget, Private healthcare, International projects, Flat structure, Small teams, Free coffee, Canteen, Bike parking, Free snacks, Free parking, In-house trainings, Modern office, No dress code.