AI Security Engineer (AI Enablement Lead)

Responsibilities: Implement and maintain security controls for AI-enabled applications, agentic workflows, and AI platforms Secure integration of AI services, APIs, and orchestration frameworks Configure and validate identity, access, secrets management, logging, and deployment safeguards Assist engineering teams with secure patterns for AI/gen AI solutions Support 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 Perform validation testing of controls and security requirements Support adversarial testing, misuse-case analysis, vulnerability assessments, and deployment readiness reviews Document findings, remediation plans, and validation results Build and maintain monitoring and alerting for AI-enabled apps and workflows Develop detection logic for suspicious prompts, unusual workflow behavior, and unauthorized access Support telemetry collection and analysis for security and compliance Assist cybersecurity ops with investigation and remediation of AI security events Partner with software engineering, platform, product, architecture, and cybersecurity teams to implement security requirements throughout the lifecycle Provide technical guidance on secure implementation practices and control requirements Participate in design reviews and implementation discussions to embed security early in delivery Assist in remediation planning for identified security findings Stay current on AI security risks, threats, and industry trends Contribute to improving AI security controls, monitoring, testing practices, and implementation standards Develop security documentation, runbooks, and operational procedures Support automation to improve security efficiency and consistency

Key requirements: Bachelor’s degree or equivalent education, training, and work-related experience Minimum of 5 years in security engineering or related cybersecurity roles Advanced knowledge in cybersecurity principles, theories, and concepts Proven experience in software development lifecycle security practices Advanced knowledge of threat modeling, security testing, and penetration testing Experience implementing and managing complex information security technologies Strong understanding of AI and LLM security concepts including prompt injection and model misuse Familiarity with AI-enabled applications, intelligent agents, and generative AI solutions Knowledge of cloud-native security principles, access control, 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 relevant security considerations Strong analytical, troubleshooting, and problem-solving skills Effective written and verbal communication skills Experience with Azure security services, Azure AI, Azure OpenAI, Azure AI Foundry, Copilot Studio, or related technologies Experience with AWS services including Amazon Bedrock Experience in regulated industries (financial services, healthcare, government) or governance/audit-driven environments Familiarity with RAG, vector databases, and AI workflow platforms Industry certifications such as Security+, Azure Security Engineer Associate, AWS Security Specialty, CISSP, GIAC, or similar effective communication problem-solving collaboration AI security concepts (prompt injection, jailbreaks, data exposure) cloud security (Azure/AWS) identity and access management