- 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
- Design and implement Infrastructure as Code (IaC) using Terraform and CloudFormation
- 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
- 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)
- 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
Requirements
- Bachelor’s degree or equivalent education, training, and work-related experience
- Minimum of 3 years of experience in security engineering or related cybersecurity roles
- Developing knowledge in cybersecurity principles, theories, and concepts
- Experience in software development lifecycle security practices
- Proficiency in implementing and managing information security technologies
- 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, and 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.
Core Competencies
Demonstrates expertise in engineering and deploying security controls for AI/ML systems, with a strong focus on Infrastructure as Code using Terraform and CloudFormation. Proficient in implementing cloud security tooling and managing CI/CD pipelines to ensure secure AI operations across AWS and Azure environments.
Highest-signal resume keywords
- Security Engineering
- Infrastructure as Code (IaC)
- Cloud Security Tooling
- CI/CD Pipeline Management
- AI/ML Security Controls
ATS Optimization Keywords
Hard Skills
- Security Engineering
- Infrastructure as Code (IaC)
- Cloud Security Tooling
- CI/CD Pipeline Management
- Python Scripting
- AI/ML Security Controls
- Technical Security Assessments
- Prompt Injection Mitigation
- Content Safety Controls
- Model Access Controls
Industry Keywords
- Cybersecurity Principles
- Software Development Lifecycle Security
- Regulated Environments
- Risk and Governance Requirements
- AI Security Standards
Tools & Technologies
- Terraform
- CloudFormation
- GitLab
- AWS
- Azure
- Wiz
- Microsoft Purview
- Sentinel
- ECS
- EKS