Job Description:
Cloud Security Engineer
POSITION SUMMARY
The Cloud Security Engineer protects Gladstone's cloud platforms and services through secure design, configuration, monitoring, automation and evidence-led remediation. The role works across Azure, Kubernetes, networks, identity, workloads, data and delivery pipelines, and supports the safe adoption of AI-enabled systems and operational tooling.
AI-ENABLED WORKING
AI-enabled working is a core expectation of this role. The role holder will use approved AI tools and automation to improve quality, speed, insight and consistency; validate outputs before acting on them; protect customer, employee and company data; and share effective practices with colleagues. Performance will be assessed through outcomes such as quality, reliability, cycle time, risk control and knowledge transfer - not simply the volume of AI-generated output or frequency of tool use.
KEY RESPONSIBILITIES
Cloud Security Engineering
• Implement and maintain security controls across Azure subscriptions, identities, networks, Kubernetes, containers, servers, data services, endpoints and CI/CD pipelines.
• Review cloud and platform designs, configuration and change proposals, identifying practical improvements before risk reaches production.
• Automate secure configuration, vulnerability detection, policy checks, evidence collection and repeatable remediation where appropriate.
• Support secrets, key, certificate, privileged-access and service-account lifecycle management.
Detection, Response and Vulnerability Management
• Monitor security signals and investigate suspicious activity, vulnerabilities, misconfiguration and control failures.
• Prioritise remediation using exploitability, exposure, business impact and compensating controls rather than severity labels alone.
• Support security incident response, evidence preservation, root-cause analysis and completion of corrective actions.
• Maintain clear security findings, owners, due dates, exceptions and risk acceptance records.
AI and Application Security
• Threat model AI-enabled services and operational agents, including data leakage, prompt injection, over-permission, unsafe actions, supplier risk and inadequate human oversight.
• Use approved AI tools to support security analysis, query development, evidence review and incident investigation while validating outputs and protecting sensitive data.
• Work with R&D and Data teams to embed secure-by-design controls into the AI-DLC, data flows, APIs and deployment patterns.
• Define and test guardrails for AI access to production systems, credentials, logs and customer information.
Compliance and Security Culture
• Provide technical evidence and remediation support for ISO 27001, PCI DSS, Cyber Essentials Plus, GDPR and customer assurance activity.
• Create practical standards, runbooks and guidance and coach engineers on secure cloud and AI-enabled working.
• Report material risks and trends clearly to the Cloud Operations Manager, Compliance Manager and relevant owners.
EXPERIENCE, KNOWLEDGE AND SKILLS
Essential
• Hands-on experience securing Azure or another major cloud platform in a production environment.
• Practical knowledge of identity and access management, networking, Kubernetes or containers, operating systems, vulnerability management, logging and incident response.
• Ability to investigate and prioritise technical security findings and implement or coordinate effective remediation.
• Experience with scripting, policy-as-code, infrastructure automation or security automation.
• Understanding of secure development and deployment practices, including secrets and pipeline security.
• Clear documentation and communication of technical risk to different audiences.
Desirable
• Azure security, Kubernetes security or recognised cyber-security certification.
• Experience of ISO 27001, PCI DSS, Cyber Essentials Plus or regulated SaaS…