The MLOps / Infrastructure Engineer AI Security is responsible for building, operating, and scaling the infrastructure and ML pipelines that support AI security research and production systems. The role ensures that AI models, datasets, experiments, and security evaluations are reproducible, observable, and deployable in a secure and compliant manner.
Key Responsibilities
- Design, build, and maintain ML pipelines for model training, evaluation, and AI security testing.
- Own infrastructure for experimentation, benchmarking, and large-scale model analysis.
- Integrate AI security checks such as scanning, verification, and unlearning workflows into ML pipelines.
- Manage compute, storage, and orchestration environments for research and platform workloads.
- Ensure reproducibility, traceability, and auditability of models, data, and experiments.
- Support deployment, monitoring, and lifecycle management of AI security tools and services.
- Collaborate with research, backend, and security teams to enable end-to-end delivery.