AI Security Engineer

About the role

NXP Semiconductors is looking for an AI Security Engineer in Austin to protect Machine Learning, Generative AI and Agentic AI solutions used in enterprise and engineering environments. The role identifies, assesses and mitigates security risks in large language models, retrieval systems, AI agents, model integrations, AI-assisted development tools and enterprise AI platforms, working with AI solution, application, cloud/platform engineering, enterprise security and architecture teams.

Key responsibilities

• Conduct security assessments of AI/ML/GenAI use cases and platforms, covering prompt injection, insecure outputs, data leakage, unsafe tool execution, model misuse and retrieval poisoning
• Support threat modeling for AI-powered applications and agentic workflows
• Help implement AI runtime security controls, monitoring and policy enforcement, and secure AI gateways, inference endpoints and service integrations
• Define security guardrails for prompts, agent execution and model interactions, and contribute to logging, auditing and anomaly detection
• Work with engineering teams to embed secure development practices and secure deployment of AI applications in cloud and enterprise environments
• Validate secure use of AI coding assistants and AI-enabled development tools
• Collaborate with architects, application and cloud security teams, data scientists and AI engineers, and guide development and product teams on security
• Support AI onboarding and security reviews, and help implement controls aligned with NIST AI RMF, OWASP Top 10 for LLM Applications and secure AI engineering best practices

Required profile

• Bachelor's degree in computer science, cybersecurity, engineering, information technology or a related field; advanced degrees preferred
• 5+ years of experience in cybersecurity, application security, cloud security or related engineering roles
• 2+ years supporting or securing AI/ML/GenAI applications or platforms
• Experience with secure software development lifecycle, application security and cloud-native security
• Familiarity with AI/ML platforms, APIs, orchestration frameworks or AI-assisted development environments

Required skills

• Understanding of LLMs, RAG architectures, embeddings, vector databases and AI agents
• Knowledge of AI attack vectors such as prompt injection and model abuse
• Familiarity with OWASP Top 10 for LLM Applications, MITRE ATLAS and NIST AI RMF
• Container security, API security, IAM, secrets management and cloud security fundamentals
• Cloud platforms such as AWS and Azure