AI Security Engineer
New
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Interra HealthHealthcare Technology
Work from anywhere in US., 10:00 AM – 3:00 PM ETFull-TimeMiddle
Salary110,000 - 165,000 USD per year
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Job Details
- Experience
- 5+ years
- Required Skills
- AWSPython
Requirements
- Bachelor's degree
- 5+ years of experience in security engineering or application security
- Hands-on experience securing AI/ML systems or LLM-based applications
- Deep knowledge of AI/ML security risks (including OWASP Top 10 for LLMs, adversarial ML, and data poisoning)
- Proven ability to test for and mitigate AI/ML security risks
- Hands-on experience with security tooling, including SAST/DAST and vulnerability scanning
- Comfortable scripting in Python or a similar language
- Understanding of cloud infrastructure (AWS, Azure, or GCP) and how AI/ML workloads are deployed and secured
- Clear communication skills with non-security stakeholders
- Experience securing systems in a regulated healthcare or life sciences environment (e.g., HIPAA, HITRUST) is nice to have
- Familiarity with LLM security frameworks (MITRE ATLAS, Garak, promptfoo) is nice to have
- Security certifications such as OSCP, GCIH, or CISSP are nice to have
Responsibilities
- 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
- Stay current on the evolving AI threat landscape and translate emerging risks into practical mitigations
- Document findings, risk assessments, and remediation plans for technical and non-technical stakeholders
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