AI Security Engineer

Lead security assessments of AI/ML models, data pipelines, and AI-enabled applications, identifying vulnerabilities such as prompt injection, model inversion, data poisoning, and adversarial inputs

Partner with Data & Analytics and Engineering to embed security requirements and threat modeling into the AI development lifecycle, from data collection through model deployment

Build and maintain guardrails, monitoring, and detection controls for AI systems in production, flagging anomalous model behavior and unauthorized data access

Conduct security reviews of third-party AI tools, APIs, and vendors, such as LLM providers, before they are integrated into Interra Health

Lead incident response for AI-related security events, including investigating suspicious model outputs or unexpected data exposure

Design and advise on standards for the secure, responsible use of AI, including data handling practices for training and inference

Test and validate access controls, encryption, and data segmentation for AI systems

Use AI tools to accelerate security testing, code review, and threat research, while modeling responsible AI use for the broader engineering organization

Stay current on the evolving AI threat landscape and translate emerging risks, such as jailbreaks and model-supply-chain risks, into practical mitigations

Document findings, risk assessments, and remediation plans clearly for both technical and non-technical stakeholders

Bachelor's degree and 5+ years in security engineering or application security, including hands-on experience securing AI/ML systems or LLM-based applications

Deep knowledge of AI/ML security risks (including the OWASP Top 10 for LLMs, adversarial ML, and data poisoning) and proven ability to test for and mitigate them

Hands-on experience with security tooling, including SAST/DAST and vulnerability scanning, and comfort scripting in Python or a similar language

Understanding of cloud infrastructure (AWS, Azure, or GCP) and how AI/ML workloads are deployed and secured within it

Clear communicator who can translate technical risk into terms non-security stakeholders understand

Comfort operating in a fast-paced, post-merger environment where systems, priorities, and structures are still evolving

Experience securing systems in a regulated healthcare or life sciences environment (e.g., HIPAA, HITRUST)

Familiarity with LLM security frameworks and tools, such as MITRE ATLAS, Garak, or promptfoo

Security certifications such as OSCP, GCIH, or CISSP

Experience red-teaming or penetration-testing AI/ML systems specifically