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- Tasks: Secure AI models and workflows, implement security tools, and conduct threat modelling.
- Company: Join DevSecAI, the world's first AI Security Enablement Company, leading in AI security solutions.
- Benefits: Enjoy a hybrid work model, global exposure, and opportunities for professional growth.
- Why this job: Be at the forefront of AI security, making a real impact across various industries.
- Qualifications: Experience in AI security engineering and familiarity with AI lifecycle management required.
- Other info: Work with cutting-edge technologies and contribute to innovative AI security research.
The predicted salary is between 48000 - 84000 £ per year.
DevSecAI is the world's first AI Security Enablement Company. We embed expert AI security engineers within clients’ teams and provide a DevSecAI SaaS platform for tracking clients' AI security tasks. We support clients globally with over 27 DevSecAI engineers deployed in the UK, US, Canada, Switzerland, Malaysia, and Singapore. We have built 6 AI Security labs that research and test the latest AI security tools before deploying to clients. We are expanding globally to become the world's leading AI Security company.
This is a full-time hybrid role for a DevSecAI Engineer located in the UK. The DevSecAI Engineer will be responsible for securing AI models, LLMs, GenAI workflows, and data pipelines using vendor-agnostic tools. They will work closely with other AI Embedded Engineers to ensure the security of AI systems throughout the AI Development Lifecycle across Finance, Government, Utilities, Health, and more.
You’ll be part of a new kind of security - embedded into client AI products, tracking their AI security maturity, implementing AI security tools within their workflows, running threat modelling workshops, red teaming against ML Models, hardening ML APIs, and helping define how the world secures artificial intelligence.
What You’ll Do
- Embed within client organisations to implement AI Security Tooling
- Perform threat modelling for GenAI & LLM-enabled apps
- Test and secure AI/ML pipelines, APIs, dashboards, and cloud infrastructure
- Help design secure-by-default AI workflows (from data ingestion to deployment)
- Work across tools like Amazon SageMaker, Azure OpenAI, AWS Bedrock, Hugging Face, etc.
- Stay ahead of AI privacy, compliance and attack trends
- Contribute to client work, internal R&D, or platform development - or all three
Qualifications
- AI Security Engineering, AI Model Security, and Data Pipeline Security skills
- Experience with various AI security tools and technologies
- Knowledge of AI lifecycle management and GenAI workflows
- Strong problem-solving and analytical skills
- Excellent written and verbal communication skills
- Ability to collaborate with cross-functional teams
Certifications
- AWS AI practitioner
- Microsoft Certified Azure AI Fundamentals
- Terraform Associate
- Cyber Agoge DevSecAI Certificate
- Cyber Agoge DevSecOps Certificate
- Microsoft AI and ML Engineering Professional Certificate
DevSecAI Engineer employer: DevSecAI
Contact Detail:
DevSecAI Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land DevSecAI Engineer
✨Tip Number 1
Familiarise yourself with the latest AI security tools and technologies mentioned in the job description, such as Amazon SageMaker and Azure OpenAI. This knowledge will not only help you during interviews but also demonstrate your proactive approach to understanding the role.
✨Tip Number 2
Engage with online communities or forums focused on AI security and DevSecOps. Networking with professionals in these spaces can provide insights into industry trends and may even lead to referrals for positions like the one at StudySmarter.
✨Tip Number 3
Consider participating in relevant workshops or webinars that focus on threat modelling and securing AI/ML pipelines. This hands-on experience can enhance your skill set and make you a more attractive candidate for the DevSecAI Engineer role.
✨Tip Number 4
Showcase your problem-solving and analytical skills through practical projects or case studies related to AI security. Having concrete examples to discuss during interviews can significantly boost your chances of landing the job with us.
We think you need these skills to ace DevSecAI Engineer
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights relevant experience in AI security engineering, data pipeline security, and any specific tools mentioned in the job description. Use keywords from the job listing to ensure your application stands out.
Craft a Compelling Cover Letter: Write a cover letter that explains why you are passionate about AI security and how your skills align with the role. Mention specific projects or experiences that demonstrate your expertise in securing AI models and workflows.
Showcase Relevant Certifications: List any relevant certifications prominently in your application. Highlight certifications like AWS AI Practitioner or Microsoft Certified Azure AI Fundamentals, as these are particularly relevant to the role.
Demonstrate Communication Skills: Since excellent written and verbal communication skills are essential for this role, ensure your application is well-structured and free of errors. Consider including examples of how you've effectively communicated complex technical concepts in previous roles.
How to prepare for a job interview at DevSecAI
✨Showcase Your Technical Skills
Make sure to highlight your experience with AI security tools and technologies during the interview. Be prepared to discuss specific projects where you've implemented security measures in AI models or data pipelines, as this will demonstrate your hands-on expertise.
✨Understand the Company’s Mission
Familiarise yourself with DevSecAI's mission and the importance of AI security in today's landscape. Being able to articulate how your values align with theirs will show your genuine interest in the role and the company.
✨Prepare for Scenario-Based Questions
Expect scenario-based questions that assess your problem-solving skills in real-world situations. Think about how you would approach threat modelling for GenAI applications or securing ML APIs, and be ready to explain your thought process.
✨Demonstrate Collaboration Skills
Since the role involves working closely with cross-functional teams, be prepared to discuss your experience collaborating with others. Share examples of how you've successfully worked in teams to achieve common goals, especially in a tech environment.
