hat You'll Do

Design and implement security controls for AI applications, agents, and LLM-powered products.
Perform threat modelling for AI systems, including prompt injection, data leakage, tool misuse, jailbreaks, and excessive agent permissions.
Secure RAG platforms, enterprise knowledge systems, and AI data flows.
Define controls for authentication, authorisation, tenant isolation, secrets management, and API security.
Build reusable security guardrails, templates, policy-as-code controls, and developer tooling.
Partner with engineering teams to embed security into CI/CD pipelines, Kubernetes environments, and release processes.
Lead vulnerability management across AI applications, APIs, containers, model-serving platforms, and supporting infrastructure.

What We're Looking For

5+ years in Security Engineering, Application Security, Cloud Security, or Platform Security.
Experience securing AI applications, AI agents, LLMs, RAG systems, or similar AI platforms.
Strong understanding of application security, API security, threat modelling, and secure software development.
Knowledge of AI security risks including prompt injection, model abuse, insecure tool usage, and data leakage.
Experience with Kubernetes, container security, CI/CD security, and software supply-chain security.
Strong knowledge of OAuth, OIDC, RBAC, ABAC, secrets management, and identity security.
Programming experience in Python, Go, or similar languages.

Responsibilities

Design and implement security controls for AI applications, agents, and LLM-powered products.
Perform threat modelling for AI systems, including prompt injection, data leakage, tool misuse, jailbreaks, and excessive agent permissions.
Secure RAG platforms, enterprise knowledge systems, and AI data flows.
Define controls for authentication, authorisation, tenant isolation, secrets management, and API security.
Build reusable security guardrails, templates, policy-as-code controls, and developer tooling.
Partner with engineering teams to embed security into CI/CD pipelines, Kubernetes environments, and release processes.
Lead vulnerability management across AI applications, APIs, containers, model-serving platforms, and supporting infrastructure.

5+ years in Security Engineering, Application Security, Cloud Security, or Platform Security.
Experience securing AI applications, AI agents, LLMs, RAG systems, or similar AI platforms.
Strong understanding of application security, API security, threat modelling, and secure software development.
Knowledge of AI security risks including prompt injection, model abuse, insecure tool usage, and data leakage.
Experience with Kubernetes, container security, CI/CD security, and software supply-chain security.
Strong knowledge of OAuth, OIDC, RBAC, ABAC, secrets management, and identity security.
Programming experience in Python, Go, or similar languages.