AI Security Test Engineer
Role Description
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.
Job Details
• Job Category: Remote
• Job Type: Full Time
• Job Location: India
• Seniority level: Mid‑Senior level
• Employment type: Full‑time
• Job function: Engineering and Information Technology
• Industries: IT Services and IT Consulting
Apply Now
Apply Now
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.
Job Details
• Job Category: Remote
• Job Type: Full Time
• Job Location: India
• Seniority level: Mid‑Senior level
• Employment type: Full‑time
• Job function: Engineering and Information Technology
• Industries: IT Services and IT Consulting
Apply Now
Apply Now