Senior AI Security Engineer

Vertex Inc. is a company focused on securing AI systems and pipelines that power their products. They are seeking a Senior AI Security Engineer to partner with various teams to identify and mitigate risks associated with AI technologies, ensuring the safe and responsible deployment of AI features.

Responsibilities:

• Perform threat modeling and security reviews of AI features, including LLM-enabled applications, RAG systems, inference pipelines, and agentic workflows
• Analyze AI systems to identify, characterize, and prioritize security vulnerabilities
• Ensure AI actions are fully traceable using industry-standard identity, security, and logging frameworks
• Perform hands-on testing and develop automated red teaming for AI and agentic features, especially focused on AI specific risks like prompt injection
• Document reproducible failure modes and partner with engineering teams to implement and verify durable mitigations
• Build or extend AI security automation and evaluation harnesses
• Define how AI agents coordinate, delegate, and escalate within security workflows
• Work with engineering to define secure-by-default patterns and guidance for AI system design, development, prompts, retrieval, tool use, output handling, deployment, logging, and least-privilege agents
• Monitor emerging AI threats, frameworks, and platform changes, and convert relevant risks into prioritized controls and mitigations
• Drive effective and secure use of AI development tooling
• Guide developers on security and privacy best practices for agentic coding, using MCP-enabled tools and hooks to help prevent vulnerabilities
• Preemptively identify and resolve technical risks and cross-team dependencies to keep AI security work on track
• Collaborate proactively with defensive security teams to enhance detection, response, and mitigation capabilities
• Act as the AI security incident SME, providing rapid triage guidance and root-cause analysis

Requirements:

• 5+ years of experience in security engineering, application security, product security, AI/ML engineering, or security architecture, with direct hands-on experience securing AI/ML or LLM-based systems
• Demonstrated ability to independently lead security reviews for complex software or AI systems and drive mitigation plans across engineering teams with limited oversight
• Practical experience assessing AI-specific risks such as prompt injection, insecure output handling, sensitive data exposure, excessive agency, model or data supply chain weaknesses, agent/tool abuse, and unsafe retrieval or memory patterns
• Advanced understanding of AI system behavior, including the ability to reason about model behavior, AI system vulnerabilities, evaluation results, and security-relevant failure modes
• Proficiency in Python (or similar) for building security automation, evaluation scripts, test harnesses, prototypes, and evidence-collection workflows
• Working knowledge of modern AI technology stacks, model APIs, orchestration frameworks, vector databases, retrieval pipelines, agentic workflows, and at least one major cloud platform (AWS, GCP, or Azure)
• Familiarity with AI security and governance frameworks such as OWASP LLM Top 10, MITRE ATLAS, NIST AI RMF, and ISO/IEC 42001
• Excellent written and verbal communication skills, with the ability to explain complex AI security risks to both technical and non-technical audiences
• Advanced degree in Computer Science, Engineering, or a related field; equivalent combination of education, training, and relevant professional experience accepted in lieu of a formal degree
• Experience leading AI red team engagements, AI test-and-evaluation activities, secure AI design reviews, or product security programs across multiple teams
• Experience deploying, integrating, or securing AI/ML systems used by customers or production engineering teams outside of a lab environment
• Hands-on experience with AI security tooling, model scanning, or custom evaluation harnesses
• Background in cloud security, IAM, application security, data protection, logging/monitoring, incident response, or security operations for production systems
• Experience coordinating practical technical work across product, platform, and security stakeholders
• External contributions, presentations, or publications in AI security, adversarial AI, AI assurance, or secure AI engineering
• Drives production outcomes through agentic, systems-level design, AI-augmented development, autonomy, mentorship, and clear communication