AI Security Engineer

Role purpose

Education and experience

• Bachelor’s degree in computer science, cybersecurity, information technology, information systems, engineering, or equivalent.
• 5+ years of cybersecurity engineering experience, including cloud, application, or data security, with practical exposure to AI/ML or agentic AI platforms.
• Postgraduate qualification in information/cyber security is an advantage.

Certifications and preferred skills

• Microsoft Azure security/AI certification such as SC-100, AZ-500, AI-102, or equivalent; AI security or ML security training and OWASP LLM knowledge.
• CISSP, CCSP, or CISM is preferred; CKS/Kubernetes or DevSecOps certification is an advantage.
• Preferred skills include Azure AI/OpenAI/ML security, cloud-native controls, IAM, managed identities, API security, secrets, network isolation, threat modeling, prompt injection testing, secure RAG, DSPM, DLP, classification, Python/PowerShell, CI/CD, and security automation.

Responsibilities

• Inventory AI/ML assets, models, inference endpoints, agents, connectors, plugins, training pipelines, and supporting infrastructure; review approved AI services.
• Assess identity, API, secrets, network, rate-limiting, data-flow, and model supply-chain risks; implement and validate least privilege, isolation, encryption, and secure secret storage.
• Validate safety filters, prompt guardrails, blocklists, and abuse controls; test for prompt attacks, jailbreaks, data leakage, model misuse, and integration abuse.
• Develop AI-specific monitoring and detection; support Shadow AI discovery and enforcement using approved CASB/SWG, endpoint, and cloud controls.
• Threat-model and test AI use cases before release and after material changes; track risks and remediation, produce assessment evidence and recommendations, and coordinate with relevant stakeholder teams.