AI Security Engineer

Role overview

Security engineering work

• Design and implement security controls in major cloud environments and complex enterprise settings.
• Secure agentic AI, LLM and generative AI, machine-learning, and AI supply-chain systems.
• Address prompt injection, jailbreaks, guardrails, RAG security, MCP and tool access, and adversarial machine-learning risks.
• Conduct threat modeling, security architecture reviews, vulnerability assessments, and adversarial or red-team testing.
• Develop scripts, tools, and automation, and work with engineering teams.
• Develop AI-enabled defensive and offensive security solutions, including detection engineering, threat intelligence, vulnerability management, and red teaming.
• Map technical controls to regulatory and policy requirements.

Minimum experience

• At least four years in cybersecurity, cloud security, application security, security engineering, DevSecOps, or security operations, including hands-on security work for generative AI, machine-learning, or emerging technologies.
• Practical experience designing and implementing security controls in major cloud environments.
• Experience with threat modeling, architecture reviews, vulnerability assessments, adversarial testing, or red teaming.
• Experience developing scripts, tools, or automation and working with engineering teams in complex enterprise environments; financial-services experience is preferred.

Education and preferred credentials

• A bachelor’s degree in cybersecurity, information security, computer science or engineering, software engineering, information technology, data science, AI, machine learning, or another relevant technical discipline is required.
• A relevant postgraduate qualification is preferred.
• Relevant cloud-security, security-operations, AI-security, or AI-governance certifications are preferred. Examples listed include Microsoft SC-100, SC-200, SC-300, AI-102, and AZ-500; CISSP, CCSP, CSSLP, OSCP, GIAC credentials; ISO/IEC 42001 Lead Implementer or Lead Auditor; AIGP, CISA, or CRISC.

AI and security knowledge

• Knowledge areas include agentic AI architectures, LLM and generative AI security, machine-learning security, prompt-injection and jailbreak defenses, guardrails, RAG security, MCP and tool-access security, adversarial ML, and AI supply-chain security.
• Familiarity with MITRE ATLAS and OWASP Top 10s is listed.

Microsoft security and cloud

• Technologies listed include Microsoft Entra, Defender XDR, Cloud and Identity, Sentinel with KQL and analytics rules, Purview for DLP and information protection, Security Copilot, agentic SOC capabilities, Azure AI Foundry, and Azure OpenAI security.

AI-driven security and governance

• AI-driven security work may include defensive and offensive engineering such as detection engineering, threat intelligence, vulnerability management, and red teaming.
• Frameworks and standards listed include ISO/IEC 27090, ISO/IEC 42001, ISO/IEC 27001, and the NIST AI Risk Management Framework.