AI Security Engineer

Senior Engineer focused on designing, building, and deploying security controls for AI systems across MetLife's enterprise. This role involves operationalizing security for AI services, accelerating safe AI adoption, evaluating AI security tools, and improving detection and risk reduction for AI usage. The engineer will work with cloud, platform, and application teams to secure AI use cases at scale, focusing on prompt protection, data protection, secure model access, and policy enforcement.

What you'd actually do

  1. Engineer and operationalize security controls for AI systems, services, and supporting infrastructure
  2. Accelerate safe adoption of AI capabilities across business and technology teams
  3. Evaluate, pilot, and implement AI security tooling and control frameworks
  4. Improve visibility, detection, and risk reduction for enterprise AI usage
  5. Provide technical guidance and implementation patterns for secure AI deployment

Skills

Required

  • 5+ years of experience in security engineering, cloud security, platform engineering, or a related technical discipline
  • Experience delivering enterprise-scale security engineering solutions in complex environments
  • Demonstrated success in a senior individual contributor role requiring strong technical ownership and cross-functional collaboration
  • Strong knowledge of cloud security fundamentals (IAM, Network segmentation, Encryption, Secrets management, Logging and monitoring, Least privilege and workload isolation)
  • Experience with modern application, platform, and infrastructure security practices
  • Working knowledge of AI/ML systems (Generative AI and LLM-based services, Agentic workflows and AI integrations, Model access patterns, APIs, and orchestration layers, AI-related architectural risks)
  • Understanding of AI security risks (Prompt injection, Sensitive data exposure, Insecure or untrusted output handling, Model misuse and excessive privilege, Third-party and supply chain risks)
  • Familiarity with OWASP Top 10 for LLM Applications

Nice to have

  • Prompt protection and filtering
  • Sensitive data protection in AI workflows
  • Secure model access and service integration
  • Policy enforcement for AI usage and governance requirements
  • Reusable AI security capabilities
  • Secure patterns for enterprise AI services, vendors, and internal use cases
  • AI hosting environments (Cloud-native platforms, Containerized and Kubernetes-based workloads)
  • Security controls into Application and deployment pipelines, AI/ML lifecycle workflows, Runtime environments
  • Technical visibility into AI system usage and behavior, Model misuse, anomalous activity, and data exposure risks
  • Develop detections and telemetry use cases for SOC and detection engineering teams
  • Reduce manual effort through automation and enrichment
  • Hands-on engineering, prototyping, and implementation activities
  • Evaluate emerging AI security tools, patterns, and techniques
  • Identify design and control gaps in AI, cloud-native, and application environments
  • Develop practical solutions that improve security while supporting speed and usability
  • Partner with Cloud and platform engineering, Application development teams, AI/ML engineering teams, Security architecture, governance, and risk stakeholders
  • Translate security requirements into implementation guidance, engineering standards, and actionable technical patterns
  • Support adoption by providing practical recommendations and technical enablement
  • Serve as a senior technical contributor and subject matter resource for AI security engineering
  • Help shape standards, guardrails, and repeatable patterns for secure AI deployment
  • Stay current with the evolving AI threat landscape, emerging architectures, and control capabilities
  • Share technical knowledge and mentor peers informally across the organization

What the JD emphasized

  • senior individual contributor
  • technically strong practitioner
  • AI security engineering
  • AI systems
  • AI use cases at scale
  • AI security tooling
  • enterprise AI usage
  • secure AI deployment
  • AI Security Engineering & Platform Controls
  • AI Environment Hardening
  • Detection, Monitoring & Response
  • Engineering Execution & Innovation
  • Cross-Functional Collaboration
  • Technical Influence
  • security engineering
  • cloud security
  • platform engineering
  • enterprise-scale security engineering solutions
  • complex environments
  • senior individual contributor role
  • strong technical ownership
  • cross-functional collaboration
  • cloud security fundamentals
  • modern application, platform, and infrastructure security practices
  • AI/ML systems
  • Generative AI and LLM-based services
  • Agentic workflows and AI integrations
  • Model access patterns, APIs, and orchestration layers
  • AI-related architectural risks
  • AI security risks
  • Prompt injection
  • Sensitive data exposure
  • Insecure or untrusted output handling
  • Model misuse and excessive privilege
  • Third-party and supply chain risks
  • OWASP Top 10 for LLM Applications
  • Secure AI e

Other signals

  • security controls for AI systems
  • secure AI use cases at scale
  • AI security tooling and control frameworks
  • visibility, detection, and risk reduction for enterprise AI usage
  • secure AI deployment