Sr. AI Security Engineer

This position will support AI workloads across our client and reduce AI risk through stronger infrastructure hygiene, in partnership with zero trust engineering. Responsibilities AI workload protection:

• Design, implement, and operate security controls for foundation model usage across Claude, ChatGPT, and Microsoft Copilot
• Support deployment and hardening of the enterprise AI gateway, including model allowlists, per-team keys, rate and budget limits, and centralized audit logging
• Engineer input and output guardrails covering prompt injection, sensitive data egress, credential and secret leakage, and unsafe output
• Integrate inline DLP and content inspection at the gateway so AI traffic is subject to the same data controls as other egress paths
• Establish security requirements for RAG architectures, including source grounding, index access control, and prevention of oversharing through model responses

Agentic and MCP security:

• Define and enforce governance for MCP servers and agent tooling, including registry, approval, and runtime policy enforcement
• Implement identity and authorization patterns for agents and other non-human identities: OAuth-based access, token lifecycle management, scoped tool permissions, and least privilege
• Assess and mitigate agentic risk classes including excessive agency, capability chaining, tool and memory poisoning, and unsafe autonomous action

Zero trust integration:

• Extend zero trust controls to AI workloads across identity, device, network, application, and data pillars
• Partner with identity engineering on conditional access, workload identity, and privileged access for AI systems and service principals
• Reduce AI risk through infrastructure hygiene: configuration baselines, egress control, secrets management, and dependency and supply chain integrity

Detection, testing, and assurance:

• Build monitoring and detection content for AI systems covering prompt behavior, tool invocation patterns, anomalous output, and data movement
• Conduct AI red team and adversarial testing, including direct and indirect prompt injection, jailbreak, and data extraction scenarios
• Perform shadow AI discovery and inventory of unsanctioned AI applications and browser extensions
• Conduct security reviews of AI applications, third-party AI vendors, and AI features in existing SaaS

Governance:

• Map controls to recognized frameworks including the OWASP Top 10 for LLM Applications, MITRE ATLAS, and the NIST AI Risk Management Framework
• Contribute to AI security standards, acceptable use guidance, architecture review criteria, and enablement material for engineering teams

7+ years in security engineering, with 2+ years securing AI, ML, or generative AI systems in production

• Hands-on experience deploying or operating an AI gateway, LLM proxy, or equivalent centralized AI control plane
• Demonstrated understanding of LLM and agent attack surface: prompt injection, data leakage through model output, insecure tool use, supply chain risk in models and dependencies
• Practical familiarity with OWASP Top 10 for LLM Applications, MITRE ATLAS, and NIST AI RMF
• Strong cloud security background, Azure preferred, including identity, network, and workload controls
• Python proficiency and comfort working with model APIs, SDKs, and evaluation tooling
• Experience partnering with engineering and data teams as an advisor rather than a gatekeeper

Preferred

• Experience with Microsoft 365 Copilot security posture, including oversharing remediation and sensitivity label enforcement
• Familiarity with AI red team tooling such as PyRIT or Garak
• Experience with MCP architecture and tool authorization patterns
• Experience with Microsoft Defender for Cloud Apps, Purview, or an equivalent DSPM platform in an AI context
• Working knowledge of ISO/IEC 42001 or the EU AI Act
• Certifications: CISSP, AZ-500, SC-100, CCSP