AI Adversarial Robustness Engineer

When to Use

Define threat models for evasion, poisoning, extraction, and inference attacks on ML/LLM systems
Design robustness evaluation suites — ASR, perturbation budgets, slice metrics, regression harnesses
Implement engineering defenses — adversarial training, input sanitization, detectors, ensembles
Run lab/staging attack campaigns on model endpoints, APIs, or batch inference (authorized only)
Audit training data and pipelines for poisoning, backdoors, and supply-chain tampering
Specify production guardrails — input validation, output filtering, rate limits, anomaly monitors
Compare certified vs empirical robustness claims and document limitations for stakeholders
Investigate robustness regressions after model updates, fine-tunes, or data refreshes