AI Security Engineer

About this opportunity

Position Title - AI Security Engineer Education Bachelor's degree in computer science, Cybersecurity, Information Technology, Information Systems, Engineering or equivalent. Postgraduate qualification in information/cyber security is an advantage. Minimum Work Experience - 5+ years of cybersecurity engineering experience, including cloud security, application security or data security, with practical exposure to AI/ML or agentic AI platforms. Skills / Certifications Microsoft Azure security/AI certifications such as SC-100, AZ-500, AI-102 or equivalent CISSP, CCSP or CISM (preferred) AI security or ML security training; OWASP LLM knowledge CKS/Kubernetes or DevSecOps certification (advantage) Preferred Skill Set Azure AI/OpenAI/ML security and cloud-native controls IAM, managed identities, API security, secrets and network isolation AI threat modelling, prompt injection testing and secure RAG patterns DSPM, DLP, classification and regulated data protection Python/PowerShell, CI/CD and security automation Job Purpose Engineer and validate security controls for AI/ML and agentic AI services used by the client, including Azure AI Services, Azure OpenAI, Azure Machine Learning, AI-enabled applications and third-party AI platforms. The role protects sensitive and regulated data, identities, models, APIs, integrations and AI infrastructure throughout the AI lifecycle. Primary Responsibilities:- Maintain an inventory of AI/ML assets, models, inference endpoints, agents, connectors, plugins, training pipelines and supporting infrastructure. Perform security reviews for Azure OpenAI, Azure AI Foundry/Services, Azure Machine Learning and approved third-party AI services. Assess authentication, authorization, managed identities, service principals, API keys, secrets, network exposure and rate-limiting controls. Implement and validate least-privilege RBAC, private endpoints, firewall rules, network isolation, encryption and secure secret storage. Assess AI data flows for PII/PHI and confidential data exposure across prompts, outputs, RAG repositories, connectors and training datasets. Configure or validate content safety filters, prompt guardrails, custom blocklists and abuse-prevention controls. Test for prompt injection, jailbreaks, insecure output handling, excessive agency, data leakage, model misuse and integration abuse. Review model registry permissions, model/version integrity, dependency provenance and AI supply-chain security. Develop AI-specific logging, monitoring and detection use cases for anomalous API activity, prompt attacks, privilege misuse and data exposure. Support Shadow AI discovery and enforcement using approved CASB/SWG, endpoint and cloud controls. Conduct threat modelling and security testing for AI use cases before production release and following material changes. Track AI security findings, risks, exceptions and remediation actions to closure; support risk acceptance where required. Produce AI security assessment reports, data exposure findings, control validation evidence and maturity recommendations. Coordinate with AI Governance, Privacy, Legal, Data, Cloud, AppSec, SOC and business owners.