AI Security Engineer

Role

The AI Security Engineer is responsible for securing the enablement and use of AI, GenAI, LLMs, and agentic technologies across the enterprise. The role drives AI security governance, risk management, technical guardrails, and operational oversight for AI systems and AI‑integrated applications across the full lifecycle—from intake and design through deployment, monitoring, and incident response. This position partners closely with global security counterparts and cross‑pillar security teams to deliver scalable, measurable, and auditable AI security controls.

Key Responsibilities

Technical Mindset & Operating Style

• Stay current on AI/LLM platforms, agent frameworks, developer tooling, and emerging attack techniques through hands‑on experimentation.
• Apply engineering intuition (software development or equivalent technical background) to architecture reviews, threat modeling, and pragmatic security guidance.
• Read, write, and review code (e.g., Python, TypeScript) to understand AI workflows, model integrations, APIs, and pipelines.
• Understand and translate developer behaviors (prompt‑driven development, agent orchestration, rapid iteration, “vibe‑coding”) into enforceable security controls.

AI Security Governance & Intake

• Own enterprise AI discovery, inventory, and intake workflows for use cases, models, tools, agents, and integrations.
• Define and enforce AI risk tiering and classification (data sensitivity, model risk, autonomy level, exposure).
• Partner with AI Governance, Legal, Privacy, and Risk to establish approval, exception, and waiver processes.

AI Threat Modeling & Risk Management

• Lead AI‑specific threat modeling (prompt injection, data leakage, model poisoning, tool abuse, agentic risk, supply‑chain threats).
• Define secure AI architecture patterns and prohibited design patterns.
• Conduct and oversee risk assessments for LLM‑integrated applications, internal copilots, and external AI services.

Technical Controls & Guardrails

• Define and operationalize guardrails including authentication/authorization, data boundaries, retention and usage controls, output/content controls, and identity/secrets/key management.
• Lead security requirements for agent frameworks, MCP servers/clients, AI gateways, and proxies.
• Partner with AppSec and Platform teams to deliver secure paved‑road AI solutions.

Secure AI Lifecycle, Testing & Monitoring

• Establish secure AI lifecycle gates (pre‑prod, prod, post‑deployment).
• Own AI security testing and validation including red teaming and abuse testing.
• Define telemetry, audit logging, and retention requirements; integrate AI signals into SIEM, detection, and incident response workflows.

Incident Response & Continuous Improvement

• Own AI‑specific detection use cases and alerting strategies.
• Partner with Incident Response teams to maintain AI incident response posture and SIEM integration.
• Lead post‑incident reviews and publish executive and operational AI security metrics and dashboards.

Requirements

Required Qualifications

• 10+ years in security architecture, application security, cloud/platform security, or related fields.
• Demonstrated experience securing AI/ML or LLM‑based systems in enterprise environments.
• Strong background in threat modeling, secure design, and risk management.
• Experience working cross‑functionally with engineering, product, legal, and compliance teams.
• Strong written and verbal communication skills, including executive‑level communication.

Preferred Qualifications

• Prior experience as a software, platform, or security engineer with significant coding responsibilities.
• Experience with AI governance frameworks or enterprise risk management programs.
• Familiarity with security testing, red teaming, and detection engineering.
• Experience building security programs with KPIs, metrics, and audit readiness.

Location & Compensation

• Location: Austin, TX and Santa Clara, CA.
• Salary range: $108,000.00 - $148,500.00.
• Time type: Full time.
• Travel: No.
• Relocation eligible: No.