Playtech is seeking a proactive AI Security Engineer (Application Security) to join its Security unit. You will be part of the Application Security team, focusing on security engineering for the AI-powered features and systems we build. Responsibilities include performing threat modelling and security design reviews for applications with an emphasis on AI and LLM-powered features; working closely with security, engineering and data science teams to identify, analyze and mitigate security vulnerabilities; reviewing the security of AI application architecture, including RAG pipelines, agent and tool-calling flows, model APIs and integrations; testing AI features adversarially for prompt injection, data leakage, jailbreaks and unsafe output handling; defining guardrails, input and output validation, and monitoring for AI systems; helping to build secure-by-default patterns and guidance so teams can ship AI features safely; supporting development by using commercial and open-source security technologies; tracking the evolving AI threat landscape and translating it into practical standards for our teams. Qualifications include: experience in application security (secure code review, threat modelling, vulnerability management); strong written and spoken English; higher education in IT or equivalent practical experience; understanding of how LLM and ML systems are built and deployed, and ability to reason about their failure modes; experience with at least one programming language (Java, Python, JavaScript, etc.) and scripting ability; strong analytical skills and problem-solving;

Playtech is seeking a proactive AI Security Engineer (Application Security) to join its Security unit. You will be part of the Application Security team, focusing on security engineering for the AI-powered features and systems we build. Responsibilities include performing threat modelling and security design reviews for applications with an emphasis on AI and LLM-powered features; working closely with security, engineering and data science teams to identify, analyze and mitigate security vulnerabilities; reviewing the security of AI application architecture, including RAG pipelines, agent and tool-calling flows, model APIs and integrations; testing AI features adversarially for prompt injection, data leakage, jailbreaks and unsafe output handling; defining guardrails, input and output validation, and monitoring for AI systems; helping to build secure-by-default patterns and guidance so teams can ship AI features safely; supporting development by using commercial and open-source security technologies; tracking the evolving AI threat landscape and translating it into practical standards for our teams. Qualifications include: experience in application security (secure code review, threat modelling, vulnerability management); strong written and spoken English; higher education in IT or equivalent practical experience; understanding of how LLM and ML systems are built and deployed, and ability to reason about their failure modes; experience with at least one programming language (Java, Python, JavaScript, etc.) and scripting ability; strong analytical skills and problem-solving;