Staff Enterprise Security Engineer, AI Security

Twilio Twilio · Enterprise · Ireland · Remote · Security

Staff Enterprise Security Engineer focused on building foundational security posture for Twilio's AI ecosystem, including technical guardrails and decision infrastructure for enterprise AI innovation. The role involves designing secure reference architectures, establishing AI vetting frameworks, and managing identity and posture for agentic AI.

What you'd actually do

  1. Design and implement secure reference architectures for Enterprise AI platforms that secures every Twilion’s engagement with them, ensuring data integrity, regulatory compliance, and resilience against evolving AI threats.
  2. Establish a definitive framework for AI vetting, driving the cultural and policy shifts needed to institutionalize this strategic mindset across the organization.
  3. Collaborate with cross functional partners to develop and set the long term roadmap for agentic AI identity and posture management, ensuring cohesive strategies for reducing risk from agentic AI use.
  4. Maintain and improve our enterprise security posture through high-quality code (Python, Go, or similar) and automated infrastructure management via IAC.
  5. Act as a technical mentor to junior engineers and a strategic advisor to leadership on the evolving AI landscape.

Skills

Required

  • 7+ years of experience in security engineering or infrastructure security
  • 2+ years of experience leading teams in a technical capacity or leading technical risk analysis in an enterprise environment.
  • Expertise in cloud security (AWS, GCP)
  • Expertise in container security (Kubernetes)
  • Proven track record of designing and deploying complex security systems at scale.
  • Strong proficiency in programming languages such as Python, Go, or Java.

Nice to have

  • Experience in building, deploying and reviewing automation for complex security workflows, including use of both AI-driven and traditional automation tools.
  • Excellent communication skills with the ability to explain complex AI security risks to non-technical stakeholders.

What the JD emphasized

  • technical leader
  • foundational security posture
  • securing the AI ecosystem
  • building the foundational "decision infrastructure"
  • technical guardrails
  • innovate with AI at scale
  • security assessments
  • threat modeling
  • identity and access control principles
  • data protection
  • architect and build preventative guardrails
  • mitigate new risks
  • first and third-party AI agents
  • secure reference architectures
  • regulatory compliance
  • evolving AI threats
  • AI vetting
  • agentic AI identity and posture management
  • reducing risk from agentic AI use
  • high-quality code
  • automated infrastructure management
  • technical mentor
  • strategic advisor
  • evolving AI landscape

Other signals

  • building foundational infrastructure for AI security
  • technical leadership in AI security
  • architecting and building preventative guardrails for AI agents