Lead AI/ML & MLOps Engineer executing projects from data foundations to model deployment. Collaborating with sales to drive AI/ML engagements for our clients.
About the role
- 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)
Benefits
- Comprehensive Private Medical Coverage
- Support for Mental Health Expenses
- Life Insurance Options
- Attractive Compensation Package
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