Requirements

• The role is suited to a senior engineer with deep expertise in AI/ML security, modern software engineering, and secure‑by‑design approaches
• Experience operating as a senior security engineer and leading engineering initiatives in a DevSecOps environment
• Strong expertise securing AI and ML systems at scale, including model lifecycle security, data pipelines, and secure deployment
• Strong hands‑on experience with machine learning stacks, such as PyTorch, TensorFlow, MLflow, Kubeflow, or equivalent managed platforms (e.g. Vertex AI, Azure ML)
• Solid understanding of cloud‑native and containerised architectures, including Kubernetes and serverless technologies
• Experience operating in complex hybrid environments, spanning public cloud, private cloud and on‑premise platforms
• Experience building automation and engineering tooling using Python, Go, or similar languages
• Certifications such as ...