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For Companies At a Glance
- Tasks: Secure AI and cloud-native workloads while collaborating with innovative teams.
- Company: Leading tech firm focused on cloud security and AI advancements.
- Benefits: Competitive salary, flexible work options, and opportunities for professional growth.
- Why this job: Join a cutting-edge team to shape the future of AI security.
- Qualifications: Experience in cloud security, especially with AWS; Azure knowledge is a plus.
- Other info: Dynamic role with potential for significant impact in the tech industry.
The predicted salary is between 60000 - 80000 £ per year.
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).
- 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.
Cloud Security Engineer Cloud/AI (LLM Security) employer: Gazelle Global Consulting Ltd
Contact Detail:
Gazelle Global Consulting Ltd Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Cloud Security Engineer Cloud/AI (LLM Security)
✨Tip Number 1
Network like a pro! Reach out to folks in the cloud security space, especially those working with AI and LLMs. Attend meetups or webinars, and don’t be shy about sliding into DMs on LinkedIn – you never know who might have the inside scoop on job openings.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects related to cloud security, especially if you've worked with AWS or Azure. This can really set you apart when chatting with potential employers or during interviews.
✨Tip Number 3
Prepare for those technical interviews! Brush up on your knowledge of cloud-native security tooling and CI/CD pipelines. Practise explaining your thought process on securing AI workloads – it’ll help you shine when discussing your experience.
✨Tip Number 4
Don’t forget to apply through our website! We’re always on the lookout for talented individuals like you. Keep an eye on our job listings and make sure your application stands out by tailoring it to the specific role.
We think you need these skills to ace Cloud Security Engineer Cloud/AI (LLM Security)
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your cloud security experience, especially with AWS and Azure. We want to see how your skills align with securing AI and LLM workloads, so don’t hold back on those relevant projects!
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to explain why you’re passionate about cloud security and how your background makes you the perfect fit for our team. Let us know what excites you about working with AI and cloud-native technologies.
Showcase Your Technical Skills: Be specific about your hands-on experience with cloud-native security tools and CI/CD pipelines. We love seeing practical examples of how you've implemented security measures in past roles, so include those juicy details!
Apply Through Our Website: We encourage you to apply directly through our website. It’s the best way for us to receive your application and ensures you don’t miss out on any important updates from our team. Plus, we can’t wait to hear from you!
How to prepare for a job interview at Gazelle Global Consulting Ltd
✨Know Your Cloud Security Basics
Make sure you brush up on your cloud security fundamentals, especially around AWS and Azure. Be ready to discuss specific security measures for AI and LLM workloads, as well as how you would implement secure architectures.
✨Showcase Your Hands-On Experience
Prepare to share examples of your hands-on experience with cloud-native security tooling and securing CI/CD pipelines. Highlight any projects where you've successfully integrated security controls into development workflows, as this will demonstrate your practical knowledge.
✨Understand the Threat Landscape
Familiarise yourself with current threats in AI and cloud environments. Be prepared to discuss threat modelling and risk assessment strategies you've used in the past, particularly in relation to AI applications and APIs.
✨Communicate Effectively
Practice articulating complex security concepts in a way that non-technical stakeholders can understand. This is crucial, as you'll need to influence engineering teams and communicate risks clearly during your role.