AI Security Engineer (AI Enablement Lead)

Truist•Charlotte, NC

About The Position

The AI Security Engineer works within the Forge AI Security organization and partners with AI engineering, application development, platform, cybersecurity, governance, and architecture teams to help ensure AI-enabled solutions are secure, resilient, compliant, and ready for operation. The role assists with designing, implementing, testing, monitoring, and validating security capabilities for AI-enabled applications, intelligent agents, retrieval-augmented generation (RAG) solutions, and enterprise AI platforms. It supports security reviews, threat modeling, adversarial testing, control validation, and deployment readiness across the AI delivery lifecycle. The position requires hands-on security engineering experience and knowledge of application security, cloud security, and AI security concepts. The engineer works with developers and solution teams to implement security controls, troubleshoot security issues, validate configurations, and improve the security posture of AI-enabled systems. The role supports no-code, low-code, and pro-code development environments, including solutions deployed on Microsoft Azure and AWS, such as Azure AI, Azure OpenAI, and Amazon Bedrock. The role also calls for a consultative approach to engaging with end users and stakeholders on proposed AI use cases. The successful candidate independently gathers user stories and business cases, builds and prioritizes use cases with limited guidance, advocates for stakeholders, and coordinates their progression through the Path to Production. They serve as a TPS/security liaison to other lines of business and maintain close working relationships with AI Enablement Leads. Success requires technical competence, consulting experience, effective communication, and the ability to navigate challenging discussions and coordinate across the organization.

Requirements

Bachelor’s degree or equivalent education, training, and work-related experience.
At least 5 years of experience in security engineering or related cybersecurity roles.
Advanced knowledge of cybersecurity principles, theories, and concepts.
Proven experience with software development lifecycle security practices.
Advanced knowledge of threat modeling, security testing, and penetration testing.
Experience implementing and managing complex information security technologies.
Understanding of AI and LLM security concepts, including prompt injection, jailbreak techniques, sensitive data exposure, tool-use risks, model misuse, and output handling concerns.
Familiarity with AI-enabled applications, intelligent agents, automation platforms, or generative AI solutions.
Knowledge of cloud-native security principles, access control, identity management, secrets handling, and secure integration design.
Understanding of monitoring, logging, alerting, and observability concepts.
Familiarity with no-code, low-code, and pro-code development environments and their security considerations.
Strong analytical, troubleshooting, and problem-solving skills.
Effective written and verbal communication skills.

Nice To Haves

At least 5 years of experience in cybersecurity engineering, application security, cloud security, software engineering, DevSecOps, or related technical disciplines.
Experience implementing security controls for enterprise applications, APIs, cloud-native solutions, or distributed systems.
Experience participating in security reviews, security testing, vulnerability management, or threat modeling activities.
Experience supporting software development lifecycle security practices.
Experience collaborating with software engineering and technology teams to address security requirements and remediation activities.
Experience implementing or supporting security controls for generative AI, AI-enabled applications, agentic systems, or intelligent automation platforms.
Experience with Microsoft Azure security services, Azure AI Services, Azure OpenAI, Azure AI Foundry, Copilot Studio, or related technologies.
Experience with AWS cloud services, including Amazon Bedrock and related AI capabilities.
Experience performing application security testing, penetration testing, vulnerability assessments, or security validation activities.
Experience working in financial services, banking, healthcare, government, or other regulated industries.
Experience supporting governance, compliance, or audit-driven technology environments.
Familiarity with retrieval-augmented generation (RAG), vector databases, agent orchestration frameworks, and AI workflow platforms.
Industry certifications such as Security+, Azure Security Engineer Associate, AWS Security Specialty, CISSP Associate, GIAC certifications, or similar credentials.
Proven consulting experience, technical competency, and ability to work through challenging stakeholder discussions.

Responsibilities

Implement and maintain security controls for AI-enabled applications, agentic workflows, intelligent automation solutions, and AI platforms.
Support secure integration of AI services, APIs, orchestration frameworks, and external tool connections.
Configure and validate identity protections, access controls, secrets management, logging, monitoring, and deployment safeguards.
Help engineering teams implement secure patterns for AI and generative AI solutions across no-code, low-code, and pro-code environments.
Participate in security architecture reviews, threat modeling, and AI security assessments.
Identify security risks, design weaknesses, and control gaps in AI-enabled solutions.
Validate the effectiveness of implemented security controls and security requirements.
Support adversarial testing, misuse-case analysis, vulnerability assessments, and deployment readiness reviews.
Document findings, recommendations, remediation activities, and validation results.
Build and maintain monitoring and alerting capabilities for AI-enabled applications and workflows.
Develop and enhance detection logic for suspicious prompts, unusual workflow behavior, unauthorized access attempts, and policy violations.
Support collection and analysis of telemetry used to monitor AI system health, security, and compliance.
Assist cybersecurity operations teams in investigating and remediating AI-related security events.
Partner with software engineering, platform, product, architecture, and cybersecurity teams to implement security requirements throughout the development lifecycle.
Provide project teams technical guidance on secure implementation practices and control requirements.
Participate in design reviews, working sessions, and implementation discussions to ensure security is considered early in delivery.
Assist with remediation planning and implementation for identified security findings.
Stay current on emerging AI security risks, threats, technologies, and industry trends.
Contribute to improvements in AI security controls, monitoring capabilities, testing practices, and implementation standards.
Assist in developing security documentation, implementation guides, runbooks, and operational procedures.
Support automation that improves security efficiency, consistency, and control effectiveness.
Meet with end users and stakeholders to gather user stories and business cases for proposed AI use cases.
Build, prioritize, advocate for, and guide AI use cases through the Path to Production.
Serve as a TPS/security liaison to other lines of business and maintain working relationships with AI Enablement Leads.
Proactively engage use case owners, ask clarifying and challenging questions, and coordinate with business lines across Truist.

Benefits

Medical insurance
Dental insurance
Vision insurance
Life insurance
Disability insurance
Accidental death and dismemberment coverage
Tax-preferred savings accounts
401(k) plan
At least 10 days of vacation during the first year, prorated as applicable
10 sick days, prorated as applicable
Paid holidays
May be eligible for a defined benefit pension plan, restricted stock units, and/or a deferred compensation plan, depending on position and division