Cybersecurity Senior Engineer (AI Cloud Security)

Responsible for developing and maintaining the technical IT/cyber security capabilities necessary for safeguarding the firm's information systems and 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 and develops 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, and preparing for future requirements to deliver reliable, scalable, and secure technology solutions with major impact on the job area., Following is a summary of the essential functions for this role. Other duties may be assigned as needed.

AI & Cloud Security Engineering

• Engineer and deploy security controls for AI/ML and Generative AI systems, including model-level, data-level, and platform-level protections.
• Implement AI guardrails and safety controls (e.g., prompt injection defenses, content safety filters, policy enforcement, model access controls).
• Support secure AI platform onboarding for internal teams, ensuring alignment with Truist AI Security Standards and Review Processes.
• Perform technical security assessments of AI systems and cloud-hosted AI services.

Infrastructure as Code & Automation

• Design and implement Infrastructure as Code (IaC) using Terraform and CloudFormation to deploy AI security controls consistently.
• Build and maintain CI/CD pipelines (GitLab) for security tooling, guardrails, and configuration-as-code.
• Automate operational workflows using Python and scripting to reduce manual security operations.

Cloud Platform Security

• Engineer secure, scalable cloud environments supporting AI workloads across AWS and Azure.
• Implement and integrate cloud security tooling (e.g., Wiz) to provide visibility and control over AI assets.
• Secure containerized and orchestrated workloads supporting AI pipelines (ECS, EKS, Kubernetes).

Collaboration & Enablement

• Partner with AI platform teams, application engineers, cloud security, and governance stakeholders to embed security into AI delivery.
• Contribute to the evolution of enterprise AI security standards, patterns, and reference architectures.
• Support incident response, threat modeling, and remediation activities related to AI systems.

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.

• Bachelor's degree or equivalent education, training, and work-related experience.
• Minimum of 7 years of experience in security engineering or related cybersecurity roles.
• Deep specialized knowledge in cybersecurity principles, theories, and concepts.
• Proven experience in software development lifecycle security practices.
• Deep knowledge of threat modeling, security testing, and penetration testing.
• Experience implementing and managing complex information security technologies.

Technical Skills & Emerging Skills Experience

• Strong hands-on experience with Azure and/or AWS
• Infrastructure as Code experience with Terraform and CloudFormation.
• Experience building and managing CI/CD pipelines (GitLab).
• Experience implementing or operating cloud security tooling (e.g., Microsoft Purview, Sentinel, Wiz or equivalent).
• Experience securing AI/ML or Generative AI systems in production environments.
• Familiarity with AI-specific security controls, such as:
• Prompt injection mitigation
• Content safety / moderation controls
• Model access and usage restrictions
• Secure data handling for AI pipelines
• Exposure to Azure and Azure-hosted AI services.
• Experience working in regulated environments with strong risk and governance requirements.