Adversarial Machine Learning Engineer conducting adversarial testing and simulations on LLM-driven AI systems for enterprise security. Collaborating with teams to validate and document findings.
Responsibilities
Conduct adversarial testing across LLM and AI-based systems
Execute real-world attack simulations, including:
- Prompt injection
- Jailbreaking and guardrail bypass
- Data exfiltration attempts
- Model inversion and evasion techniques
- RAG manipulation
Develop scripts and tooling to automate attack scenarios
Analyse model behaviour under adversarial pressure
Identify systemic vulnerabilities in:
- APIs
- Embedding pipelines
- Vector databases
- Fine-tuned model implementations
Collaborate with engineering teams to validate remediation
Document findings clearly and concisely
Ensure AI systems are resilient before deployment at scale.
Requirements
Strong experience in adversarial ML or AI security research
Experience working with LLM-based systems (OpenAI, Anthropic, open-source models, etc.)
Deep understanding of:
- Prompt injection techniques
- Model jailbreak methodologies
- AI system exploitation vectors
Strong Python skills
Experience building custom attack tooling or experimentation frameworks
Familiarity with:
- RAG architectures
- Vector databases
- Model fine-tuning workflows
- API-based model deployments
Understanding of model safety mechanisms and guardrails
Background in cybersecurity or penetration testing (Nice to Have)
Familiarity with OWASP LLM Top 10 (Nice to Have)
Experience working in enterprise environments (Nice to Have)
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