Your responsibilities:

Deliver cloud security engineering capability focused on securing AI, LLM, and cloud-native workloads, with AWS as the primary environment and Azure as a secondary platform.

Implement secure cloud architectures and controls, ensuring AI/LLM workloads comply with organisational security standards and cloud security policies.

Work with architects and AI engineering teams to define secure patterns for LLM deployments, AI agents, and model pipelines across cloud environments.

Engineer and operationalise cloud-native security tooling, including IaC security, secrets management, container security, and monitoring solutions.

Integrate security controls into CI/CD pipelines and modern development workflows, enabling secure and automated deployment of cloud and AI workloads.

Participate in threat modelling, risk assessment, and security design reviews for AI applications, APIs, and cloud services.

Support evaluation and onboarding of emerging AI security tools and cloud-native security capabilities, contributing to technology selection and capability uplift.

Essential skills/knowledge/experience:

Strong background in cloud security engineering, ideally with deep experience on AWS; Azure exposure is highly beneficial.

Hands-on experience or working exposure in securing LLM/AI workloads, including model deployment, data flows, and runtime considerations.

Proficiency with cloud-native security tooling (CSPM, CWPP, secrets management, logging/monitoring, container security).

Experience securing IaC and CI/CD pipelines using tools such as Terraform, CloudFormation, GitHub Actions, GitLab, or similar.

Knowledge of IAM design, network security controls, encryption, secrets management, and cloud identity principles.

Understanding of modern cloud architectures (serverless, microservices, managed AI/ML services) and their associated security risks.

Ability to collaborate effectively with AI engineers, developers, and cloud teams to ensure secure implementation of AI workloads.

Desirable skills/knowledge/experience:

Experience securing GenAI, Agentic AI, vector databases, model APIs, or data pipelines used by LLMs.

Knowledge of responsible AI principles, model governance, or AI-specific threat modelling (e.g., adversarial ML, data poisoning, prompt injection). xbpsjku

Background working in regulated industries such as Financial Services or Insurance.

Strong stakeholder communication skills, including the ability to influence engineering teams and articulate cloud/AI security risks clearl