Director of AI Security Engineering

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 Director of AI Engineering will drive the solutions and participate in engineering for the following:

• Drive technical engineering for operating technical and governance controls that help secure MetLife enterprise AI systems, vendor products and supporting infrastructure. 
• Speed the safe adoption of AI deployment at MetLife to enable increased efficiency, better customer satisfaction, and market leadership
• Operationalize and globally scale AI Security tooling suite to secure enterprise usage of AI with appropriate controls and tools without slowing the deployment
• Execute engineering for cutting edge Proof of Concepts for new tools taht are changing rapidly to stay ahead of our industry competitors
• Ensuring we drive continuous improvement by identifying ways to harden environments, improve detection and response, and reduce manual operational effort.

• Lead a small team of dedicated professionals to provide global solutions for protecting MetLife AI functions.
• Lead the technical engineering for standardization and keep aware of the latest changes in the AI environment
• Harden AI hosting environments, integrating security tooling into AI and application workflows, improving detection and monitoring, validating security configurations, and partnering with regional and response teams to drive risk reduction. 
• The successful candidate is someone with strong cloud and security fundamentals, solid understanding of AI and AI security, and enough hands-on engineering and leadership experience to help implement and support controls directly in development, platform, or Kubernetes-based environments.
• Identify solutions for security gaps in cloud-native and AI-related environments.
• Partner with security engineers, cloud/platform teams, developers, and AI engineers, and clearly communicate implementation requirements, risks, and remediation guidance.

• Minimum 5 years of relevant experience in Technology and leading teams
• Candidate demonstrates a strong understanding of core cloud security concepts such as identity and access management, network segmentation, secrets management, encryption, logging and monitoring, workload isolation, secure configuration, and least privilege across modern cloud environments.
• Demonstrates a working understanding of how generative AI, machine learning, and agentic AI systems are built, deployed, and integrated, along with core risks such as prompt injection, sensitive data exposure, insecure output handling, model misuse, excessive agency, supply chain risk, and weak access controls. The candidate should also understand MCP and A2A architectures, including risks associated with each layer. 
• Experience implementing, tuning, or supporting security controls and tools in production environments, including monitoring, posture management, vulnerability management, data protection, identity, runtime security, or detection capabilities.
• Experience with container security and cloud foundation fundamentals, including images, pods, namespaces, RBAC, secrets handling, network policies, admission controls, and common misconfiguration risks.
• Experience with secure handling of sensitive data used by or exposed to AI systems, including data classification, access boundaries, privacy considerations, retention concerns, and data loss prevention concepts.
• Curiosity and willingness to learn evolving AI technology

• Experience leading teams or personally hardening Kubernetes environments, securing container supply chains, implementing admission or policy controls, or supporting runtime security in containerized platforms.
• Experience working with guidance such as OWASP Top 10 for LLM applications, AI risk management frameworks, or secure AI development practices.
• Hands-on experience in software development, platform engineering, DevSecOps, or Kubernetes administration. The candidate should have a solid understanding of code, deployment pipelines, infrastructure configuration, or cluster operations to help implement and validate security controls.
• Hands-on familiarity with model deployment pipelines, inference services, feature or data pipelines, vector or retrieval systems, prompt orchestration, or AI gateways.
• Ability to use scripting or automation to improve operational efficiency, validate configurations, enrich detections, or support repeatable security checks.
• Background in building detections, tuning alerts, investigating suspicious behavior, or responding to security events across cloud-native systems.
• Familiarity with image scanning, dependency management, SBOM concepts, artifact signing, and integrity validation for code and model artifacts
• Qualifications: CCSP or similar, SC-100, CKA/CKS, or AI-related security certifications (AI-900) is a plus

The expected salary range for this position is $140,000 - $210,000. 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.