AI Security Engineer: Secure AI/ML Lifecycle

VulnU; Columbia (MD); Hybrid; USD 103,000 - 137,000; Full time

Benefits offered by this job

Paid parental leave

Retirement match

Equity

Job summary

Tenable is seeking an AI Information Security Engineer to design and maintain security controls across the AI/ML lifecycle. You will establish guardrails for LLMs, SLMs, and open-source models, monitor AI threat vectors, and contribute to security architectures and tooling across enterprise AI deployments.

Work with software engineers and product teams to integrate security best practices into AI development, training, validation, and deployment processes.

Qualifications

• 2+ years of professional information security or application security experience with AI/ML focus.
• Knowledge of AI security threats: adversarial ML, data poisoning, prompt injection, model evasion, and inference attacks.
• Understanding of SDLC/DevSecOps and security testing (SAST/DAST/SCA) practices.
• Experience with modern web apps, Docker, and microservices architectures.
• Strong written and verbal communication to translate AI security risks.

Responsibilities

• Design, implement, and maintain end-to-end security controls for AI/ML lifecycles.
• Develop security guardrails for LLMs, SLMs, and open-source models.
• Monitor AI threats and vulnerabilities; build governance and model integrity controls.
• Create security guidance, training, and reference architectures for AI deployments.
• Collaborate with engineering and product teams to embed security in AI development and deployment processes.

Skills

AI security

Threat modeling

Security testing

Docker & microservices

Python/Go

Cloud security

Communication

Education

Bachelor's Degree in CS/Cybersecurity/Data Science

Tools

Burp Suite

ZAP

Tenable WAS

PyTorch

Hugging Face Transformers

TensorFlow

Docker

Job description

Tenable is seeking an AI Information Security Engineer to design and maintain security controls across the AI/ML lifecycle. You will establish guardrails for LLMs, SLMs, and open-source models, monitor AI threat vectors, and contribute to security architectures and tooling across enterprise AI deployments.

Work with software engineers and product teams to integrate security best practices into AI development, training, validation, and deployment processes.