Cloud Security Engineer – Cloud/AI (LLM Security)

Overview

• 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.

Qualifications

• 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.
• 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).
• 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 clearly.

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