Senior AI Security Engineer

***This role is 5 days a week in the Atlanta or Charlotte Office***

The Senior AI Security Engineer helps design, implement, test, and operate the controls that keep enterprise AI systems safe, governed, and production ready.

This role focuses on the security engineering foundations required for AI-enabled applications, agents, prompt-driven workflows, and tool-integrated automations operating in a regulated enterprise environment.

This is a hands-on engineering role within the Forge AI Security & Governance model.

The engineer supports guardrail implementation, prompt-injection defense, output filtering, monitoring, secure tool-use boundaries, logging, detection content, and deployment-readiness controls for AI-enabled systems.

The work spans design, testing, automation, detection engineering, and operational support across the AI delivery lifecycle.

Daily work includes implementing security controls for AI and agentic systems, validating configurations, supporting adversarial test preparation, building monitoring logic, partnering with engineering to harden prompt and tool behaviors, documenting controls, and ensuring AI solutions meet enterprise safety, traceability, and governance requirements before and after deployment.

Following is a summary of the essential functions for this role. Other duties may be assigned as needed.

- 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 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.

- 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.

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.

- 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.

- 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.

- Experience working in regulated environments with strong risk and governance requirements.

- 3+ years of experience in security engineering, cybersecurity operations, application security, or a closely related technical discipline.

- Hands-on experience implementing technical controls for enterprise software, APIs, cloud-native services, or automation workflows.

- Working knowledge of AI/LLM security concepts such as prompt injection, unsafe output handling, tool-use abuse, sensitive data exposure, and control boundary enforcement.

- Experience with logging, alerting, monitoring, or detection content for identifying suspicious or policy-violating behavior in applications or workflows.

- Understanding of access control, identity boundaries, secrets handling, secure integration design, and environment-based deployment controls.

- Ability to work with engineering teams to translate security concerns into implementable guardrails, validations, and release controls.

- Strong written documentation and communication skills, especially for controls, findings, remediation evidence, and technical guidance.

- Experience operating within enterprise governance, security, and release-management practices where evidence-based deployment readiness matters.

- Experience with AI or agentic security controls, prompt and output protection strategies, or security validation of LLM-enabled features.

- Experience with Microsoft, Azure, Copilot / Copilot Studio, or AI-enabled enterprise workflow platforms.

- Experience with adversarial testing, red teaming support, detection engineering, or misuse-case validation for AI-enabled systems.

- Experience in financial services, cybersecurity, regulated enterprise environments, or platforms with high audit and control requirements.

- Familiarity with secure tool-calling patterns, API protections, model or prompt change validation, and runtime traceability for AI systems.

- Working knowledge of cloud-native security patterns, telemetry analysis, and deployment gating for modern engineering teams.