AI Security Engineer

Applied Materials•Austin, TX

7d•$108,000 - $148,500

About The Position

The AI Security Engineer is responsible for securing the enablement and use of AI, GenAI, LLM, and agentic technologies across the enterprise, balancing business velocity with protection of Applied Materials’ intellectual property, sensitive data, and customer trust. This 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. The role serves as a key focal point for AI security execution in the US and partners closely with global counterparts and cross‑pillar security teams to deliver scalable, measurable, and auditable AI security controls.

Requirements

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
Highly technology‑savvy and continuously current on rapidly evolving AI/LLM platforms, agent frameworks, developer tooling, and emerging attack techniques through hands-on experimentation and learning.
Brings strong engineering intuition through prior software development experience or equivalent hands-on technical background, enabling effective architecture reviews, threat modeling, and pragmatic security guidance.
Comfortable reading, writing, and reviewing code (e.g., Python, TypeScript, or similar) to understand AI workflows, model integrations, APIs, pipelines, and real‑world failure modes.
Practical experience experimenting with AI tooling, copilots, agents, and “vibe‑coding” workflows, with an understanding of how developers’ prototype, iterate, and ship AI‑enabled systems.
Able to translate modern developer behaviors (prompt‑driven development, agent orchestration, rapid iteration) into realistic, enforceable security controls rather than theoretical policy.
Uses technical credibility to influence engineering teams, accelerate adoption of secure AI patterns, and ensure security enables—rather than blocks—innovation.

Nice To Haves

Prior experience as a software engineer, platform engineer, 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 clear KPIs, metrics, and audit readiness

Responsibilities

Own enterprise AI discovery, inventory, and intake workflows covering AI 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 teams to establish approval, exception, and waiver processes
Ensure AI security controls align with enterprise risk management and audit expectations
Lead AI‑specific threat modeling, including prompt injection, data leakage, model poisoning, tool abuse, agentic risk, and 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
Track AI security risks and exceptions through remediation and closure
Define and operationalize AI security guardrails, including: Authentication and authorization for AI systems Data boundaries, retention, and usage controls Output/content controls and policy enforcement Identity, secrets, and key management for AI workloads
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 for engineering teams
Establish secure AI lifecycle gates (pre‑prod, prod, post‑deployment)
Own AI security testing and validation, including red teaming, abuse testing, and guardrail effectiveness
Define requirements for telemetry, audit logging, and retention for AI sessions, tool calls, and memory usage
Integrate AI signals into SIEM, detection, and incident response workflows
Own AI‑specific detection use cases and alerting strategies
Partner with IR teams to develop and maintain AI incident response posture and integration with SIEM tools
Lead post‑incident reviews and drive control improvements
Publish executive and operational AI security metrics and dashboards