AI Security Engineer
Key Skills for This Role
Full Job Posting
Minimum experience
- At least four years in cybersecurity, cloud security, application security, security engineering, DevSecOps, or security operations.
- Hands-on experience securing generative AI, machine-learning, or emerging technology solutions.
- Practical cloud security-control design and implementation experience.
- Experience with threat modeling, security architecture reviews, vulnerability assessments, adversarial or red-team testing, scripting, automation, and enterprise engineering teams.
- Financial-services experience is preferred.
Minimum qualifications
- Bachelor’s degree in cybersecurity, information security, computer science, computer engineering, software engineering, information technology, data science, artificial intelligence, machine learning, or another relevant technical discipline.
- A related postgraduate qualification is preferred.
Professional qualifications
- Relevant cloud security, security operations, AI security, or AI governance certifications are preferred.
- Examples include Microsoft SC-100, SC-200, SC-300, AI-102, AZ-500; CISSP, CCSP, CSSLP, OSCP, GIAC; ISO/IEC 42001 Lead Implementer or Lead Auditor; AIGP, CISA, and CRISC.
AI, agentic AI, and ML security
- Knowledge areas include agentic AI architectures, LLM and generative AI security, ML security, prompt injection and jailbreak defenses, guardrails, RAG security, MCP and tool-access security, adversarial machine learning, and AI supply-chain security.
- Familiarity with MITRE ATLAS and OWASP Top 10 resources.
Microsoft security and cloud
- Relevant technologies include Microsoft Entra; Defender XDR, Cloud, and Identity; Sentinel, KQL, and analytics rules; Purview DLP, information protection, and data governance; Security Copilot; Azure AI Foundry; and Azure OpenAI security.
AI-driven cybersecurity
- Developing AI solutions for defensive and offensive security engineering, including detection engineering, threat intelligence, vulnerability management, and red-teaming.
Governance and risk
- Relevant frameworks and standards include ISO/IEC 27090, ISO/IEC 42001, ISO/IEC 27001, and the NIST AI Risk Management Framework.
- Ability to map technical controls to regulatory and policy requirements.
Responsibilities
- Design and implement security controls for generative AI, machine-learning, and other emerging technology solutions in major cloud environments.
- Conduct threat modeling, security architecture reviews, vulnerability assessments, and adversarial or red-team testing.
- Develop scripts, tools, and automation to support security engineering.
- Work with engineering teams in complex enterprise environments to secure AI and cloud solutions.
- Develop AI solutions for defensive and offensive security engineering, including detection engineering, threat intelligence, vulnerability management, and red-teaming.
- Map technical security controls to regulatory and policy requirements.
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