AI Application Security Engineer

About the role

NXP Semiconductors is looking for an AI Application Security Engineer in Austin to protect AI-powered applications, APIs, generative AI (GenAI) solutions and retrieval-augmented generation (RAG) systems used in enterprise and engineering environments. The role focuses on the application layer, covering authentication and authorization, prompt handling, API integrations, data access patterns, session management and secure interaction with enterprise systems. It works closely with application development, AI engineering, enterprise security and architecture teams.

Key responsibilities

• Conduct application security assessments and secure design reviews for AI-powered applications and GenAI solutions
• Review AI integrations involving APIs, plugins, RAG pipelines, vector databases and enterprise applications
• Evaluate risks such as prompt injection, insecure outputs, data leakage, insecure APIs and retrieval poisoning
• Support secure software development lifecycle (SSDLC) practices and secure coding practices
• Implement application-layer controls and secure authentication and authorization mechanisms
• Support AI application penetration testing, API security testing and remediation
• Help validate secure use of AI coding assistants and AI-enabled development tools
• Implement controls aligned with NIST AI RMF, OWASP Top 10 for LLM Applications and secure application engineering practices

Required profile

• Bachelor's degree in computer science, cybersecurity, engineering, information technology or a related field; advanced degrees preferred
• 5+ years in cybersecurity, application security, runtime security or related engineering roles
• 2+ years supporting or securing AI/ML/GenAI applications or platforms
• Experience with SSDLC, security testing and cloud-native security practices
• Familiarity with AI/ML platforms, APIs, orchestration frameworks or AI-assisted development environments
• Cloud platform experience such as AWS or Azure

Required skills

• LLMs, RAG architectures, APIs, vector databases and AI-assisted applications
• Secure coding, API security, authentication and authorization, tenant isolation
• Prompt injection, insecure outputs, insecure plugins and retrieval poisoning
• OWASP Top 10 for LLM Applications, OWASP ASVS, MITRE ATLAS, NIST AI RMF
• Cloud-native security, container security, IAM, secrets management