Job Purpose

The Manager: AI Security Engineer is responsible for designing, building, implementing, and operating security controls that protect the organization’s Artificial Intelligence (AI), Machine Learning (ML), and Large Language Model (LLM) platforms and applications.

This is a hands-on engineering role focused on developing and integrating AI security technologies, including policy engines, AI guardrails, input and output filtering, AI firewalls, secure API gateways, and AI security automation. The role translates findings from security architecture, threat modelling, and security assurance activities into technical security controls that strengthen the AI environment.

Working closely with AI Engineering, Information Security, Cloud Engineering, Enterprise Architecture, Product Teams, Data Science, and AI Security Assurance teams, the role embeds security into AI platforms and delivery pipelines using secure-by-design and DevSecOps principles.

Key Performance Areas (KPAs)

1. AI Security Engineering

• Design, develop, and implement enterprise AI security controls, including:
  • AI guardrails
  • Policy engines
  • Input and output filtering
  • Content moderation
  • AI firewalls
• Develop reusable security patterns for AI and Large Language Model (LLM) environments.
• Continuously improve AI security capabilities based on evolving threats and business requirements.

2. Policy-as-Code & Security Automation

• Design and maintain Policy-as-Code frameworks to govern:
  • AI model access
  • Data handling
  • Tool and plugin invocation
  • Output validation
• Implement and manage policy engines using technologies such as OPA/Rego or equivalent authorization frameworks.
• Develop automation for policy deployment, version control, and lifecycle management.

3. AI Guardrails & Secure AI Operations

• Configure, optimize, and maintain AI guardrails that balance:
  • Security
  • Safety
  • Performance
  • User experience
• Continuously monitor and tune security controls to minimize false positives while maintaining effective protection.
• Develop standardized AI protection patterns for enterprise use cases.

4. API & AI Gateway Security

• Engineer secure AI gateway and API gateway configurations, including:
  • Authentication
  • Authorization
  • Rate limiting
  • Schema validation
  • Prompt and response inspection
  • Secure API communication
• Implement secure communication standards between AI services and enterprise applications.

5. Secure AI Platform Engineering

• Develop secure-by-design architecture patterns for AI implementations, including:
  • Secure Retrieval-Augmented Generation (RAG)
  • Secrets management
  • Agent and plugin sandboxing
  • AI service isolation
• Integrate AI security controls into MLOps, LLMOps, CI/CD, and DevSecOps pipelines.
• Enable AI security controls to be deployed, managed, and monitored as code.

6. Monitoring, Detection & Telemetry

• Implement enterprise monitoring and logging capabilities for AI platforms.
• Develop telemetry for:
  • Prompt and response logging
  • Threat detection
  • Abuse detection
  • Model monitoring
  • Security event collection
• Improve AI observability through automation and centralized monitoring.

7. Security Control Improvement

• Collaborate with AI Security Assurance teams to remediate security findings.
• Enhance AI security controls based on:
  • Red-team exercises
  • Security assessments
  • Threat intelligence
  • Emerging attack techniques
• Maintain enterprise AI security baselines aligned with recognized security frameworks and Zero Trust principles.

8. Technical Leadership & Enablement

• Provide technical guidance to engineering teams on implementing AI security controls.
• Promote secure coding practices for AI development.
• Develop reusable security libraries, reference architectures, and implementation standards.
• Support continuous improvement of AI security engineering capabilities across the organization.

Governance

AI Security Governance

• Contribute to the establishment and maintenance of AI security engineering standards.
• Monitor security engineering performance through metrics such as:
  • Number of AI security controls deployed
  • Coverage of AI solutions by standardized security controls
  • Guardrail effectiveness
  • Reduction in AI-related security incidents
  • Time required to develop and deploy new security controls
• Ensure AI security controls align with enterprise cybersecurity governance and regulatory requirements.

Escalation Management

• Investigate and resolve issues relating to:
  • AI security control failures
  • Guardrail bypasses
  • Policy engine failures
  • AI platform security incidents
  • Cross-platform security dependencies

Reporting

• Provide regular reports to leadership covering:
  • AI security engineering initiatives
  • Security control implementation
  • Operational performance
  • Risk mitigation activities
  • Project status
• Prepare ad hoc reports for strategic initiatives and executive management as required.

Budget Responsibilities

• Support planning and management of AI security engineering platforms, policy engines, security tooling, and associated operational and capital expenditure.

Job Requirements

Education

• Bachelor’s or Master’s Degree in:
  • Computer Science
  • Software Engineering
  • Information Security
  • Cybersecurity
  • Information Technology
  • Or a related discipline

Professional Certifications (Preferred)

• Cloud Security Certifications (Azure, AWS, or Google Cloud)
• Secure Software Development or DevSecOps certifications
• AI or Machine Learning Security certifications
• Certified Information Systems Security Professional (CISSP)
• Certified Secure Software Lifecycle Professional (CSSLP)
• Other relevant cybersecurity certifications

Experience

• Minimum 8–10 years’ experience in Security Engineering or Software Engineering.
• Minimum 5 years’ experience designing, implementing, and operating enterprise security controls.
• Strong software engineering and automation experience, preferably using Python.
• Demonstrated experience developing:
  • AI guardrails
  • Policy engines
  • Content filtering solutions
  • API security controls
• Experience with:
  • Policy-as-Code frameworks (OPA/Rego or equivalent)
  • Authorization engines
  • AI platforms
  • Large Language Models
  • AI gateways
  • API gateways
  • MLOps and LLMOps environments
• Experience implementing secure data pipelines incorporating:
  • Encryption
  • Tokenization
  • Data masking
  • Secure data handling

Technical Competencies

AI Security Engineering

• AI security controls
• AI guardrails
• AI firewalls
• Policy engines
• Input and output filtering
• Content moderation