AI Security Engineer

The AI Security Engineer at NXP Semiconductors will be responsible for securing Machine Learning (ML), Generative AI (GenAI), and Agentic AI solutions deployed across enterprise and engineering environments. This role focuses on identifying, assessing, and mitigating security risks associated with Large Language Models (LLMs), retrieval systems, AI agents, model integrations, AI-assisted development tools, and enterprise AI platforms.

The AI Security Engineer will collaborate with AI solution teams, application teams, cloud/platform engineering, enterprise security, and architecture teams to ensure solutions are deployed securely and aligned with NXP security standards, risk management practices, and regulatory requirements.

The ideal candidate will have hands-on experience in application security, cloud-native security, and secure software engineering practices, with strong understanding of AI-related attack vectors such as prompt injection, insecure tool usage, model abuse, data leakage, retrieval poisoning, and insecure model integrations.

This role will support AI security assessments, runtime security controls, AI security testing, and implementation of scalable security guardrails to enable secure enterprise AI adoption and AI-enabled engineering workflows.

Your Opportunity

NXP is a global player in Semiconductor industry, and security is an essential and integral part of our business.

Job Responsibility

1.AI Security Assessments

• Conduct security assessments for AI/ML/GenAI use cases and platforms
• Evaluate AI systems for risks related to prompt injection, insecure outputs, data leakage, unsafe tool execution, model misuse, and retrieval poisoning
• Review AI integrations involving APIs, plugins, MCP servers, external tools, vector databases, and enterprise data sources
• Support threat modeling activities for AI-powered applications and agentic workflows

AI Runtime Security and Control Implementation

• Support the implementation of AI runtime security controls, monitoring, and policy enforcement mechanisms
• Assist in securing AI gateways, inference endpoints, and AI service integrations
• Help define security guardrails for prompts, agent execution, and model interactions
• Contribute to observability and monitoring capabilities for AI systems, including logging, auditing, and anomaly detection

3.Secure AI Development and Engineering Practices

• Collaborate with engineering teams to integrate secure development practices into AI/ML/GenAI workflows
• Support secure deployment and configuration of AI applications across cloud and enterprise environments
• Assist with validating secure use of AI coding assistants and AI-enabled development tools

4.Collaboration and Stakeholder Engagement

• Collaborate with Enterprise Architects, Application Security teams, Cloud Security teams, Data Scientists, AI Engineers, and Business stakeholders
• Support AI onboarding and security review processes for new AI initiatives and platforms
• Provide security guidance and recommendations to development and product teams
• Participate in discussions related to AI governance, risk management, and enterprise AI adoption

5.Risk Management and Compliance

• Support implementation of controls aligned with industry frameworks such as NIST AI RMF, OWASP Top 10 for LLM Applications, and secure AI engineering best practices
• Identify and support remediation of security risks associated with AI systems, models, datasets, and third-party AI services
• Assist in evaluating AI solutions for compliance with internal security requirements and applicable regulations
• Support security investigations related to AI systems and integrations

• Bachelor’s degree in computer science, Cybersecurity, Engineering, Information Technology, or a related field
• Advanced degrees such as Masters are preferred, * 5+ years of experience in Cybersecurity, Application Security, Cloud Security, or related engineering roles
• 2+ years of experience supporting or securing AI/ML/GenAI applications or platforms
• Experience with secure software development lifecycle (SSDLC), application security, and cloud-native security practices
• Experience with AI/ML platforms, APIs, orchestration frameworks, or AI-assisted development environments
• Familiarity with cloud platforms such as AWS, Azure.

Technical Skills

• Understanding of AI/ML/GenAI concepts including LLMs, RAG architectures, embeddings, vector databases, and AI agents
• Knowledge of AI-related attack vectors and security risks including prompt injection, indirect prompt injection, insecure tool access, and model abuse
• Familiarity with OWASP Top 10 for LLM Applications, MITRE ATLAS, and NIST AI RMF concepts
• Knowledge of container security, API security, IAM, secrets management, and cloud security fundamentals
• Understanding of secure coding and software security principles, * Strong analytical and problem-solving skills
• Excellent communication and collaboration abilities
• Ability to manage multiple initiatives in a fast-paced environment