Job Description

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.

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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 (e.g., scanning, verification, 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.

Required Technical Skills

- Strong experience with MLOps platforms, ML pipelines, and workflow orchestration tools.
- Hands-on experience with containerisation and orchestration (Docker, Kubernetes, or equivalent).
- Experience managing cloud or on-prem compute environments for ML workloads.
- Familiarity with experiment tracking, model registries, and data versioning tools.
- Understanding of security, compliance, and access control considerations in ML systems.
- Strong scripting and programming skills (Python preferred).

Qualifications & Experience

Education:
- Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field.

Experience:
- 5–8+ years of experience in MLOps, ML infrastructure, or platform engineering roles.
- Proven experience supporting ML research or production ML systems at scale.

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