AI Security Test Engineer

The AI Security Test Engineer is responsible for identifying, evaluating, and mitigating security risks specific to AI-driven systems. This role focuses on validating the security, robustness, privacy, and trustworthiness of AI/ML models, pipelines, and integrations across the SDLC. The engineer applies adversarial thinking, risk analysis, and human judgment to uncover vulnerabilities that traditional security testing may miss.

Key Responsibilities

• Assess security risks across AI/ML systems, including data pipelines, models, APIs, and deployments.
• Design and execute security test strategies for AI systems (pre- and post-deployment).
• Perform adversarial testing, including prompt injection, data poisoning, model inversion, and membership inference.
• Validate access controls, authentication, authorization, and API security for AI services.
• Test AI systems for privacy leakage, data exposure, and compliance risks (PII, regulated data).
• Evaluate model robustness against misuse, abuse, and malicious manipulation.
• Collaborate with data scientists, ML engineers, developers, and security teams to remediate findings.
• Analyze AI supply-chain risks (datasets, pre-trained models, third-party APIs).
• Define security acceptance criteria and risk thresholds for AI releases.
• Document vulnerabilities clearly with business impact and remediation guidance.
• Stay current with emerging AI threats, attack vectors, and regulatory expectations.

Required Skills & Experience

• Strong background in application security, penetration testing, or security engineering.
• Experience testing APIs, cloud-based systems, and distributed architectures.
• Solid understanding of AI/ML concepts (training, inference, models, datasets).
• Knowledge of common AI security threats (prompt injection, hallucinations, bias exploitation).
• Hands-on experience with security testing tools and techniques.
• Ability to think adversarially and beyond documented requirements.
• Strong analytical and risk-based thinking skills.
• Excellent communication skills to explain complex risks to non-technical stakeholders.

Preferred Qualifications

• Experience with LLMs, GenAI platforms, or ML model deployment.
• Familiarity with OWASP Top 10 for LLM Applications and AI security frameworks.
• Experience testing AI in regulated industries (finance, healthcare, insurance).
• Background in privacy, compliance, or ethical AI validation.
• Scripting or automation skills (Python, Bash, or similar).

Key Traits

• High attention to detail with strong investigative mindset.
• Comfortable challenging assumptions and design decisions.
• Business-aware: understands impact of AI failures on trust, revenue, and reputation.
• Independent thinker with strong ownership mentality.

Success in This Role Looks Like

• AI security risks are identified early, not after production incidents.
• Clear visibility into AI-specific vulnerabilities and business impact.
• Strong collaboration between security, QA, and AI engineering teams.
• Reduced AI-related incidents, data leaks, and reputational risks.

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.