Senior Associate AI Security Engineer

Truist•Atlanta, GA

22h•Hybrid

About The Position

The Senior Associate AI Security Engineer 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. The Senior Associate 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 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.

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, 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.
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.

Nice To Haves

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

Responsibilities

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.