Assess LLM/GenAI applications for security risks including prompt injection, data leakage, model abuse, and hallucination risks
Evaluate AI/ML pipelines, RAG workflows, and API‑driven LLM integrations for vulnerabilities
Support secure agent design, misuse prevention controls, and guardrail implementation
Review AI system telemetry, logs, and monitoring insights to detect anomalies or unsafe behaviours
Conduct threat modelling, risk assessments, and scenario‑based adversarial testing for GenAI workloads
Evaluate API security, data protection, and identity controls across AI applications
Contribute to security documentation, governance guidelines, and responsible AI usage standards
Review cloud configurations and enforce secure access, IAM, logging, monitoring, and remediation controls
Work with engineering teams to ensure AI workloads adhere to cloud security best practices
Analyze cloud‑native AI service integrations for misconfigurations or exposure risks

4–7 years of experience in Cybersecurity, Application Security, Cloud Security, or SOC/IR, with exposure to AI/ML workloads
Strong understanding of LLMs, prompt engineering basics, GenAI risks, and threat vectors
Experience with at least one cloud platform (AWS preferred) in a security capacity
Familiarity with AI‑related risks such as prompt injection, data exfiltration, model manipulation, hallucinations, and access misuse
Hands‑on exposure to security monitoring tools, API security, logging, and cloud‑native detection

Gen AI Security Engineer

Key skills