AI Security Engineer
Interra Health
Description
- 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
Eligibility Criteria
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Bachelor's degree and 5+ years in security engineering or application security, including hands-on experience securing AI/ML systems or LLM-based applications
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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
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Hands-on experience with security tooling, including SAST/DAST and vulnerability scanning, and comfort scripting in Python or a similar language
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Understanding of cloud infrastructure (AWS, Azure, or GCP) and how AI/ML workloads are deployed and secured within it
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Clear communicator who can translate technical risk into terms non-security stakeholders understand
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Comfort operating in a fast-paced, post-merger environment where systems, priorities, and structures are still evolving
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Experience securing systems in a regulated healthcare or life sciences environment (e.g., HIPAA, HITRUST)
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Familiarity with LLM security frameworks and tools, such as MITRE ATLAS, Garak, or promptfoo
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Security certifications such as OSCP, GCIH, or CISSP
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Experience red-teaming or penetration-testing AI/ML systems specifically
About Interra Health
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