Minimum ExperienceAt least 4 years of experience in cybersecurity, cloud
security, application security, security engineering, DevSecOps or security operations, including 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 modelling, 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, preferably in financial services.
Minimum QualificationsBachelor’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.
Postgraduate qualification in Cybersecurity, Artificial Intelligence, Machine Learning, Computer Science, Software Engineering, or a related technical discipline is preferred.
Professional QualificationsRelevant professional certifications in cloud security, security operations, AI security or AI governance are preferred. Examples include Microsoft SC-100, SC-200,
SC-300, AI-102 and AZ-500; CISSP, CCSP, CSSLP, OSCP, relevant GIAC certifications (for example GPCS, GCSA, GMLE), ISO/IEC 42001 Lead Implementer or
Lead Auditor, AI governance certifications such as AIGP, and CISA or CRISC.
Knowledge and Skills
AI, Agentic AI and ML Security
Agentic AI architectures, LLM and Generative AI security,
ML security, prompt injection and jailbreak defences, guardrails, RAG security, MCP and tool-access security, adversarial machine learning, and AI supply chain
security. Familiarity with MITRE ATLAS, the OWASP Top 10s.
