***This role is 5 days a week in Charlotte or Atlanta Office*** The Cybersecurity Engineer (AI Cloud Security) is part of Truist’s AI Security Engineering function and is responsible for designing, engineering, deploying, and operating security controls for AI, ML, and Generative AI systems across cloud platforms. This role focuses on hands on engineering, enabling secure AI usage through cloud-native security controls, automation, and infrastructure as code, and operationalizing enterprise AI security standards. Engineers in this role work closely with AI platform teams, cloud engineering, governance, and risk partners to ensure AI systems are secure by design, compliant by default, and scalable across the enterprise. Designs advanced technical and cybersecurity capabilities across all phases of the software development lifecycle, including threat modeling, security testing, and penetration testing. Plans, builds, and enhances cybersecurity technologies by baselining systems, analyzing trends, andpreparing forfuture requirements to deliver reliable, scalable, and secure technology solutions with significant impact on the job area. ESSENTIAL DUTIES AND RESPONSIBILITIES Following is a summary of the essential functions for this job. Other duties may be performed, both major and minor, which are not mentioned below. Specific activities may change from time to time. AI & Cloud Security Engineering 3 Engineer and deploy security controls for AI/ML and Generative AI systems, including modellevel, datalevel, and platformlevel protections. 3 Implement AI guardrails and safety controls (e.g., prompt injection defenses, content safety filters, policy enforcement, model access controls). 3 Support secure AI platform onboarding for internal teams, ensuring alignment with Truist AI Security Standards and Review Processes. 3 Perform technical security assessments of AI systems and cloudhosted AI services. Infrastructure as Code & Automation 3 Design and implement Infrastructure as Code (IaC) using Terraform and CloudFormation to deploy AI security controls consistently. 3 Build and maintain CI/CD pipelines (GitLab) for security tooling, guardrails, and configurationascode. 3 Automate operational workflows using Python and scripting to reduce manual security operations. Cloud Platform Security 3 Engineer secure, scalable cloud environments supporting AI workloads across AWS and Azure. 3 Implement and integrate cloud security tooling (e.g., Wiz) to provide visibility and control over AI assets. 3 Secure containerized and orchestrated workloads supporting AI pipelines (ECS, EKS, Kubernetes). Collaboration & Enablement 3 Partner with AI platform teams, application engineers, cloud security, and governance stakeholders to embed security into AI delivery. 3 Contribute to the evolution of enterprise AI security standards, patterns, and reference architectures. 3 Support incident response, threat modeling, and remediation activities related to AI systems. Required Qualifications The requirements listed below are representative of the knowledge, skill and/or ability required. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions. 3 Bachelors degree or equivalent education, training, and work-related experience. 3 Minimum of 5 years of experience in security engineering or related cybersecurity roles. 3 Advanced knowledge in cybersecurity principles, theories, and concepts. 3 Proven experience in software development lifecycle security practices. 3 Advanced knowledge of threat modeling, security testing, and penetration testing. 3 Experience implementing and managing complex information security technologies. Technical Skills & Emerging Skills Experience 3 Strong handson experience with Azure and/or AWS 3 Infrastructure as Code experience with Terraform and CloudFormation. 3 Experience building and managing CI/CD pipelines (GitLab). 3 Experience implementing or operating cloud security tooling (e.g., Microsoft Purview, Sentinel, Wiz or equivalent). 3 Experience securing AI/ML or Generative AI systems in production environments. 3 Familiarity with AIspecific security controls, such as: 3 Prompt injection mitigation 3 Content safety / moderation controls 3 Model access and usage restrictions 3 Secure data handling for AI pipelines 3 Exposure to Azure and Azurehosted AI services. 3 Experience working in regulated environments with strong risk and governance requirements.