MetLife

As part of MetLife’s Global Security team, you’ll work alongside world-class experts to protect MetLife, our customers and our colleagues. The team is responsible for managing cybersecurity, IT risks and vulnerabilities, physical security, and more. In this fast-paced, mission-driven environment, you’ll join outstanding teammates to expand your skills, collaborate across the organization and implement innovative approaches to safeguard MetLife when it matters most. Ready to make an impact? Join us if you want to embrace the rapidly evolving environment and use transformative technology to integrate and build security into the foundation of key initiatives across MetLife.

The Senior Engineer – AI Security Engineering will serve as a senior individual contributor focused on the design, build, deployment, and continuous improvement of security controls for AI systems across the enterprise.

This is a hands-on engineering role for a technically strong practitioner who can partner across security, cloud, platform, and application teams to secure AI use cases at scale.

Core Focus Areas

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

1. AI Security Engineering & Platform Controls

• Engineer and operationalize controls for: 
  • Prompt protection and filtering
  • Sensitive data protection in AI workflows
  • Secure model access and service integration
  • Policy enforcement for AI usage and governance requirements
• Contribute to the design and implementation of reusable AI security capabilities across enterprise environments
• Help define secure patterns for enterprise AI services, vendors, and internal use cases

2. AI Environment Hardening

• Secure AI hosting environments, including: 
  • Cloud-native platforms
  • Containerized and Kubernetes-based workloads
• Integrate security controls into: 
  • Application and deployment pipelines
  • AI/ML lifecycle workflows
  • Runtime environments
• Validate configurations, reduce attack surface, and improve control effectiveness

3. Detection, Monitoring & Response

• Improve technical visibility into: 
  • AI system usage and behavior
  • Model misuse, anomalous activity, and data exposure risks
• Partner with SOC and detection engineering teams to: 
  • Develop detections and telemetry use cases
  • Improve monitoring coverage for AI platforms and workflows
  • Reduce manual effort through automation and enrichment

4. Engineering Execution & Innovation

• Lead hands-on engineering, prototyping, and implementation activities
• Evaluate emerging AI security tools, patterns, and techniques through proof-of-concept work
• Identify design and control gaps in AI, cloud-native, and application environments
• Develop practical solutions that improve security while supporting speed and usability

5. Cross-Functional Collaboration

• 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

6. Technical Influence

• 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

• 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

Core Technical Expertise

• Strong knowledge of cloud security fundamentals, one or more of the following, 
  • 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, one or more of the following: 
  • Generative AI and LLM-based services
  • Agentic workflows and AI integrations
  • Model access patterns, APIs, and orchestration layers
  • AI-related architectural risks including MCP and A2A interaction patterns

AI Security Knowledge

• Understanding of AI security risks, one or more of the following: 
  • 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
  • Secure AI engineering practices
  • AI risk and governance concepts

Hands-On Engineering Capability

• Experience in one or more of the following: 
  • DevSecOps
  • Platform engineering
  • Infrastructure automation
  • Security engineering for cloud-native applications
• Working knowledge of: 
  • Kubernetes and container security
  • Pods, RBAC, secrets, and network controls
  • Policy enforcement and configuration validation
• Ability to write or review scripts and automation in support of validation, telemetry, or operational efficiency

Data Security

• Experience helping secure sensitive data in modern application or AI contexts, one or more of the following: 
  • Data classification
  • Access controls
  • Privacy boundaries
  • Retention concepts
  • DLP-related requirements

Mindset

• Strong curiosity and adaptability in a rapidly evolving technical landscape
• Practical problem solver with strong engineering judgment

• Effective communicator able to work across technical and non-technical stakeholders

• Experience: 
  • Hardening Kubernetes and cloud-native environments at scale
  • Securing software or container supply chains, including SBOM, signing, or integrity validation
  • Implementing runtime security controls and policy enforcement
• Familiarity with AI ecosystem components such as: 
  • Model pipelines and inference services
  • Vector databases and RAG systems
  • AI gateways, proxy layers, and prompt orchestration frameworks
• Exposure to: 
  • Detection engineering and alert tuning
  • Incident triage in cloud-native or distributed environments
  • Security automation for operational efficiency
    
• Certifications (Preferred)
  • CCSP or equivalent cloud security certification
  • Microsoft SC-100
  • CKA or CKS
  • AI-related certifications (e.g., AI-900 or similar)

The expected salary range for this position is $120,000- $180,000. This role may also be eligible for annual short-term incentive compensation and stock-based long-term incentives. All incentives and benefits are subject to the applicable plan terms.