U

University of Washington

Public research university in Seattle, WA

AI Security Engineer

Security Engineering & ArchitectureConfirmed live in the last 24 hours
Full-timeSeattle, WA, USA$87.6k - $142.4k /yr

About the Job

Requirements

  • A bachelor's degree in Cybersecurity, Information Security, Computer Science, Information Technology, or a related field, or an equivalent combination of education and experience.
  • At least 3 years of experience in cloud security engineering, DevSecOps, or infrastructure security with hands-on cloud platform experience.
  • Hands-on experience with Azure security services such as Defender for Cloud, Sentinel, Entra ID/RBAC, Azure Policy, Key Vault, and network security configurations; equivalent depth in AWS or GCP with ability to ramp on Azure is also accepted, and Azure experience is strongly preferred.
  • Understanding of Zero Trust architecture principles, identity governance, and conditional access.
  • Experience with container or Kubernetes security concepts.
  • Proficiency in Python, PowerShell, or Bash for security automation.
  • Experience with SIEM tools such as Azure Sentinel or equivalent, and incident response.
  • Working knowledge of at least two compliance frameworks among FERPA, HIPAA, NIST 800-53/800-171, or SOC 2.
  • Strong written and verbal communication skills and ability to explain security concepts to technical and other audiences.

Responsibilities

  • Implement and maintain security controls for AI platforms in Microsoft Azure, including network security groups, firewalls, encryption, key management, and secure landing zones aligned with the Azure Well-Architected Framework security pillar and Microsoft cloud security benchmark.
  • Configure and manage identity and access management using Entra ID, role-based access control, conditional access policies, and Zero Trust principles across management-group and subscription hierarchies.
  • Implement and maintain infrastructure-as-code security using Bicep and/or Terraform, including policy-as-code enforcement and infrastructure-as-code scanning in continuous integration and continuous delivery pipelines.
  • Embed security into continuous integration and continuous delivery pipelines using GitHub Advanced Security or equivalent, including static application security testing, dynamic application security testing, software composition analysis, container image scanning, and auto-remediation workflows.
  • Develop security automation scripts and tools using Python, PowerShell, or Bash to streamline vulnerability scanning, configuration hardening, and compliance evidence collection.
  • Support security of AI application-layer components for Purple and nebulaONE, including retrieval-augmented generation data isolation, least-privilege tool/function-call authorization, agent action budgets and rate limits, output filtering, and secrets isolation.
  • Participate in recurring red-team exercises against AI platforms mapped to the OWASP LLM Top 10, OWASP Top 10 for Agentic Applications, and MITRE ATLAS; document findings and support remediation.
  • Assess and help mitigate AI-specific security risks including prompt injection, jailbreak attacks, data leakage through model outputs, and adversarial attacks.
  • Support guardrails for large language model and agent application patterns, including retrieval-augmented generation, tool/function calling, Model Context Protocol, and multi-agent orchestration workflows.
  • Apply AI security governance practices aligned with the NIST AI Risk Management Framework, NIST Generative AI Profile, and ISO/IEC 42001.
  • Produce and maintain compliance evidence, not policy, for FERPA, HIPAA where protected health information is in scope, GLBA, NIST 800-171/CMMC, and SOC 2 as it relates to AI platforms and cloud infrastructure.
  • Support vendor security oversight of Cloudforce and Microsoft, including HECVAT completion/review, VPAT assessment, SOC 2 report analysis, data processing agreement reviews, and security questionnaire management.
  • Monitor AI platform security posture using Azure Sentinel, writing and tuning KQL queries for detection rules, alert triage, and threat hunting.
  • Maintain and execute incident response playbooks for AI platforms, including data breaches, unauthorized access, prompt injection attacks, model compromise, and agent misuse scenarios.
  • Triage and investigate security incidents, coordinate response activities with the UWIT Office of Information Security, and contribute to post-incident reports with root cause analysis.
  • Contribute reusable, pre-approved paved-road security templates and configurations that make secure practices easier for developers and administrators.
  • Develop and maintain security documentation including architecture diagrams, runbooks, standard operating procedures, and incident response playbooks.
  • Evaluate emerging security tools and technologies and make recommendations for adoption.
  • Participate in an on-call rotation for critical AI platform security incidents and adjust hours as needed for security events, business needs, and deadlines.

Desired Qualifications

  • Microsoft Certified: Cloud and AI Security Engineer Associate certification.
  • SC-100 Cybersecurity Architect Expert or SC-200 Security Operations Analyst certification.
  • HashiCorp Terraform Associate certification.
  • Experience with infrastructure-as-code using Bicep and/or Terraform, including infrastructure-as-code scanning and policy-as-code concepts.
  • Experience embedding security into continuous integration and continuous delivery pipelines, including static application security testing, dynamic application security testing, software composition analysis, or container scanning.
  • CISSP or CISM certification.
  • Exposure to AI/ML application security risks such as prompt injection defense, retrieval-augmented generation data isolation, agent authorization, and output filtering.
  • Familiarity with the OWASP LLM Top 10, OWASP Top 10 for Agentic Applications, or MITRE ATLAS.
  • Knowledge of AI governance frameworks including the NIST AI Risk Management Framework, NIST Generative AI Profile, and ISO/IEC 42001.
  • Experience with the Azure Well-Architected Framework security pillar and Microsoft cloud security benchmark.
  • Production multi-tenant SaaS or AI-platform operations experience.
  • Azure cost-management and FinOps awareness.
  • Experience in higher education or public sector IT.
  • Experience with HECVAT, VPAT, and vendor security assessment processes.
  • Familiarity with penetration testing methodologies and tools.
  • Knowledge of the Washington My Health My Data Act, GLBA, or GDPR.
  • Experience with Agile/Scrum methodologies and tools such as Jira or Azure Boards.

About the Company

University of Washington

Public research university in Seattle, WA

The University of Washington is a public research university founded in 1861, with campuses in Seattle, Bothell, and Tacoma. It provides undergraduate, graduate, and professional education alongside research and public service.

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