AI Security Engineer
Key Skills for This Role
Full Job Posting
Experience
- At least 4 years of experience in cybersecurity, cloud security, application security, security engineering, DevSecOps, or security operations.
- Hands-on experience securing generative AI, machine-learning, or other emerging technology solutions.
- Practical experience designing and implementing security controls in major cloud environments.
- Experience with threat modeling, security architecture reviews, vulnerability assessments, and adversarial or red-team testing.
- Experience developing scripts, tools, or automation and working with engineering teams in complex enterprise environments; financial services experience is preferred.
Education and certifications
- Bachelor's degree in cybersecurity, information security, computer science or engineering, software engineering, IT, data science, AI, machine learning, or another relevant technical discipline.
- A postgraduate qualification in a related technical discipline is preferred.
- 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 certifications; ISO/IEC 42001 Lead Implementer or Lead Auditor; AIGP, CISA, and CRISC.
AI, agentic AI, and ML security
- Knowledge of 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 machine learning, and AI supply-chain security.
- Familiarity with MITRE ATLAS and OWASP Top 10s.
Microsoft security and cloud
- Knowledge of Microsoft Entra, Defender XDR, Cloud and Identity, Sentinel including KQL and analytics rules, Purview including DLP and information protection, and Security Copilot and agentic SOC capabilities.
- Knowledge of Azure AI Foundry and Azure OpenAI security.
AI-driven cybersecurity
- Develop AI solutions for defensive and offensive security engineering, such as detection engineering, threat intelligence, vulnerability management, and red teaming.
Governance and risk
- Knowledge of 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 AI, machine-learning, cloud, and other emerging technology solutions.
- Assess and secure agentic AI architectures, LLM and generative AI systems, machine-learning systems, RAG, MCP, and AI supply chains.
- Address prompt injection, jailbreaks, guardrails, tool-access security, and adversarial machine-learning threats.
- Develop AI-enabled defensive and offensive security solutions, including detection engineering, threat intelligence, vulnerability management, and red teaming.
- Use and support Microsoft security and cloud capabilities, including Entra, Defender, Sentinel, Purview, Security Copilot, Azure AI Foundry, and Azure OpenAI security.
- Map technical security controls to regulatory and policy requirements and apply relevant AI governance and risk frameworks.
- Develop scripts, tools, and automation and collaborate with engineering teams in complex enterprise environments.
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