Job Description
Reporting to Technology Manager, the AI Security Engineer will support the security, governance, and compliance of artificial intelligence (AI) initiatives at the university and its three campuses. The AI Security Engineer exists to secure the university's AI platforms, primarily Purple, built on Cloudforce nebulaONE and hosted in Azure AI Foundry, ensuring that AI services delivered to over 50,000 faculty, staff, and students across three campuses operate within a robust, compliant, and trustworthy security framework.
Key Responsibilities
[30%] Security Engineering
-Implement and maintain security controls for AI platforms within Microsoft Azure, including network security groups, firewalls, encryption, key management, and secure landing zones aligned with the Azure Well-Architected Framework (security pillar) and the Microsoft cloud security benchmark.
- Configure and manage identity and access management using Entra ID, RBAC, conditional access policies, and Zero Trust architecture principles across management-group and subscription hierarchies.
[20%] Security Automation & Infrastructure-as-Code (DevSecOps).
- Implement and maintain infrastructure-as-code (IaC) security using Bicep and/or Terraform, including policy-as-code enforcement (Azure Policy, Sentinel policies) and IaC scanning in CI/CD pipelines.
- Embed security into CI/CD pipelines (DevSecOps) using GitHub Advanced Security or equivalent, including SAST, DAST, SCA, container image scanning, and auto-remediation workflows.
- Develop security automation scripts and tools (Python, PowerShell, Bash) to streamline vulnerability scanning, configuration hardening, and compliance evidence collection.
[20%] AI Application Security & Red-Teaming
- Support the security of AI application-layer components specific to Purple and nebulaONE, including RAG 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, the OWASP Top 10 for Agentic Applications, and MITRE ATLAS, documenting findings and supporting remediation.
- Assess and help mitigate AI-specific security risks including prompt injection, jailbreak attacks, data leakage through model outputs, and adversarial attacks.
- Support implementation of guardrails for LLM and agent application patterns including RAG, tool/function calling, MCP (Model Context Protocol), and multi-agent orchestration workflows.
- Apply AI security governance practices aligned with the NIST AI Risk Management Framework (AI RMF), the NIST Generative AI Profile, and ISO/IEC 42001.
[15%] Compliance Evidence & Vendor Oversight
- Produce and maintain compliance evidence (not policy) for FERPA, HIPAA (where PHI is in scope, including areas outside UW Medicine), 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.
[10%] Incident Response & Security Monitoring
- Monitor AI platform security posture using Azure Sentinel (SIEM), writing and tuning KQL queries for detection rules, alert triage, and threat hunting.
- Maintain and execute incident response playbooks specific to 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 UWIT Office of Information Security, and contribute to post-incident reports with root cause analysis.
[5%] Paved-Road Patterns, Documentation & Continuous Improvement
- Contribute to 'paved-road' security patterns -- reusable, pre-approved templates and configurations that make the secure path the easy path 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; make recommendations for adoption.
Required Qualifications
To be considered for this opportunity your application must demonstrate you meet both the minimum qualifications and additional qualifications listed below. Equivalent education and/or experience may substitute for minimum qualifications except when there are legal requirements, such as a license, certification, and/or registration.
Minimum Qualifications
-Bachelor's Degree in Cybersecurity, Information Security, Computer Science, Information Technology, or a related field, or equivalent combination of education and experience.
-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. Candidates with equivalent depth in AWS or GCP who can demonstrate the ability to ramp on Azure are also encouraged to apply; 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 (Azure Sentinel or equivalent) and incident response.
- Working knowledge of at least two compliance frameworks: FERPA, HIPAA, NIST 800-53/800-171, or SOC 2.
- Strong written and verbal communication skills with the ability to explain security concepts to both technical
Applicants who do not meet these qualifications WILL NOT be forwarded to the Hiring Manager.
Preferred Qualifications
-Microsoft Certified: Cloud and AI Security Engineer Associate
- SC-100 (Cybersecurity Architect Expert), SC-200 (Security Operations Analyst).
- HashiCorp Terraform Associate certification.
- Experience with infrastructure-as-code (Bicep and/or Terraform), including IaC scanning and policy-as-code concepts.
- Experience embedding security into CI/CD pipelines: SAST, DAST, SCA, or container scanning.
- CISSP or CISM (note: CISSP requires 5 years experience, which may be aspirational for mid-level candidates).
- Exposure to AI/ML application security risks: prompt injection defense, RAG data isolation, agent authorization, output filtering.
- Familiarity with OWASP LLM Top 10, OWASP Top 10 for Agentic Applications, or MITRE ATLAS.
- Knowledge of AI governance frameworks: NIST AI RMF, NIST Generative AI Profile, 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 Washington My Health My Data Act, GLBA, GDPR.
- Experience with Agile/Scrum methodologies and tools (Jira, Azure Boards).
Working Conditions
This is a hybrid position with two days in office per week (one required team day on Wednesday, one flex day chosen by the employee). Work is conducted in an open office environment with daily interactions with team members, subject matter experts, and stakeholders at all levels of the organization.
While the general working hours are Monday through Friday, 8:00 AM to 5:00 PM, the AI Security Engineer will participate in an on-call rotation for critical AI platform security incidents and may need to adjust hours to accommodate security events, business needs, and deadlines.
This role is pivotal in ensuring that AI platforms and services operate within a robust security framework that aligns with institutional policies, federal regulations, and industry best practices. As a core member of the AI Platforms team, the AI Security Engineer will be responsible for developing and enforcing security standards, conducting risk assessments, managing compliance requirements, and collaborating across teams to embed security into every phase of the AI lifecycle.
Compensation, Benefits and Position Details
Pay Range Minimum:
$87,624.00 annual
Pay Range Maximum:
$142,392.00 annual
Other Compensation:
Benefits:
For information about benefits for this position, visit https://www.washington.edu/jobs/benefits-for-uw-staff/
Shift:
First Shift (United States of America)
Temporary or Regular?
This is a regular position
FTE (Full-Time Equivalent):
100.00%
Union/Bargaining Unit:
Not Applicable
