Negotiable
Undetermined
Remote
Remote
Summary: The Senior AI Security & Testing Engineer will act as a technical authority in securing and validating enterprise AI systems. This role involves leading efforts in AI model testing, vulnerability management, and penetration testing, ensuring AI capabilities are safe and compliant. The position requires a blend of security expertise and hands-on AI engineering within complex IT environments. The role is fully remote and spans a duration of 12 months.
Key Responsibilities:
- Design and execute comprehensive test strategies for AI/ML models, including functional testing, adversarial testing, red teaming, hallucination detection, and model drift analysis.
- Build automated AI test harnesses and pipelines to validate model performance, reliability, and safety at scale.
- Evaluate LLMs, generative AI systems, and predictive models for robustness, bias, and misuse potential.
- Identify, assess, and prioritize vulnerabilities across AI systems, APIs, data pipelines, and model deployment environments.
- Lead remediation planning with engineering, DevOps, and security teams.
- Maintain vulnerability dashboards, metrics, and reporting aligned with enterprise risk frameworks.
- Conduct threat modeling for AI systems, including model extraction, prompt injection, data poisoning, and supply chain risks.
- Develop and run simulation environments to test AI behavior under stress, adversarial conditions, and real world attack scenarios.
- Perform penetration testing on AI enabled applications, model endpoints, and orchestration layers.
- Create synthetic attack scenarios to evaluate system resilience and incident response readiness.
- Collaborate with red teams and blue teams to integrate AI specific attack vectors into enterprise security exercises.
- Architect and implement integrations between AI tools, security platforms, and enterprise IT systems.
- Build workflows that connect AI models with monitoring, logging, SIEM, SOAR, and DevSecOps pipelines.
- Evaluate and integrate third party AI security tools, model governance platforms, and testing frameworks.
- Ensure AI systems comply with enterprise architecture standards, data governance policies, and regulatory requirements.
Key Skills:
- 7+ years in cybersecurity, penetration testing, or security engineering, with at least 2+ years focused on AI/ML systems.
- Strong proficiency in Python, security automation, and AI/ML testing frameworks.
- Hands on experience with LLMs, vector databases, model deployment platforms, and MLOps pipelines.
- Deep understanding of adversarial ML, model vulnerabilities, and AI specific threat landscapes.
- Expertise with vulnerability scanners, SAST/DAST tools, penetration testing suites, and cloud security platforms.
- Experience integrating AI systems with enterprise IT (APIs, microservices, identity systems, logging, monitoring, etc.).
- Familiarity with NIST AI RMF, OWASP Top 10 for LLMs, and emerging AI security standards.
Salary (Rate): £90,000 yearly
City: undetermined
Country: undetermined
Working Arrangements: remote
IR35 Status: undetermined
Seniority Level: undetermined
Industry: IT
Job Title:
Senior AI Security & Testing Engineer
Location:
Remote
Duration:
12 Months
Position Overview:
As a Senior AI Security & Testing Engineer, you will serve as a technical authority responsible for securing, validating, and stress?testing enterprise AI systems. You will lead efforts across AI model testing, adversarial simulation, vulnerability management, penetration testing, and orchestration of AI tools within complex enterprise IT environments. This role blends deep security expertise with hands?on AI engineering, ensuring that AI?driven capabilities are safe, resilient, compliant, and seamlessly integrated into existing infrastructure.
Key Responsibilities:
AI Testing & Evaluation
Design and execute comprehensive test strategies for AI/ML models, including functional testing, adversarial testing, red teaming, hallucination detection, and model drift analysis.
Build automated AI test harnesses and pipelines to validate model performance, reliability, and safety at scale.
Evaluate LLMs, generative AI systems, and predictive models for robustness, bias, and misuse potential.
Vulnerability Management
Identify, assess, and prioritize vulnerabilities across AI systems, APIs, data pipelines, and model deployment environments.
Lead remediation planning with engineering, DevOps, and security teams.
Maintain vulnerability dashboards, metrics, and reporting aligned with enterprise risk frameworks.
Conduct threat modeling for AI systems, including model extraction, prompt injection, data poisoning, and supply chain risks.
Simulation & Penetration Testing
Develop and run simulation environments to test AI behavior under stress, adversarial conditions, and real world attack scenarios.
Perform penetration testing on AI enabled applications, model endpoints, and orchestration layers.
Create synthetic attack scenarios to evaluate system resilience and incident response readiness.
Collaborate with red teams and blue teams to integrate AI specific attack vectors into enterprise security exercises.
AI Tool Orchestration & Enterprise Integration
Architect and implement integrations between AI tools, security platforms, and enterprise IT systems.
Build workflows that connect AI models with monitoring, logging, SIEM, SOAR, and DevSecOps pipelines.
Evaluate and integrate third party AI security tools, model governance platforms, and testing frameworks.
Ensure AI systems comply with enterprise architecture standards, data governance policies, and regulatory requirements.
Required Qualifications
7+ years in cybersecurity, penetration testing, or security engineering, with at least 2+ years focused on AI/ML systems.
Strong proficiency in Python, security automation, and AI/ML testing frameworks.
Hands on experience with LLMs, vector databases, model deployment platforms, and MLOps pipelines.
Deep understanding of adversarial ML, model vulnerabilities, and AI specific threat landscapes.
Expertise with vulnerability scanners, SAST/DAST tools, penetration testing suites, and cloud security platforms.
Experience integrating AI systems with enterprise IT (APIs, microservices, identity systems, logging, monitoring, etc.).
Familiarity with NIST AI RMF, OWASP Top 10 for LLMs, and emerging AI security standards.
Preferred Qualifications
Experience with red team operations or offensive security research.
Background in building AI evaluation frameworks or automated testing systems.
Certifications such as OSCP, OSWE, CEH, CISSP, or AI focused credentials.
Experience with Kubernetes, cloud native architectures, and secure model deployment.
Knowledge of data governance, privacy engineering, and secure data lifecycle management.