Job
Description
We are seeking an experienced Application Security Architect to join our growing Secure Architecture team. In this role, you will focus on designing secure solutions for modern application architectures including AI/ML workloads, APIs and cloud-native systems across hybrid environments. You will partner with cross-functional teams to embed security into solution designs, champion secure development practices and align architecture with Resmed s overall security strategy. This role is ideal for someone who thrives on innovation, thinks like an adversary and believes secure design is critical to scalable growth. Let s talk about responsibilities: Lead security architecture efforts for AI, API and cloud-based applications across AWS, Azure and GCP platforms. Define and evaluate security controls to protect models from adversarial attacks, data poisoning and unauthorized inference. Develop secure design patterns, reusable controls and architecture blueprints aligned with Resmed s security standards. Partner with data scientists and engineering teams to develop secure ML pipelines, including feature engineering, validation, and anomaly detection mechanisms. Design secure-by-design architecture patterns and reusable controls for APIs, microservices, and distributed cloud environments. Guide the implementation of zero-trust architecture principles across systems, including identity, access, networking, application and data flows. Perform threat modeling, risk assessments and architecture reviews on high-impact projects. Establish detection and response capabilities around AI systems and APIs. Engage with stakeholders across the business to ensure security solutions enable agility, innovation, and growth. Let s talk about you: 10+ years in cybersecurity, with strong experience in cloud-native application and data security. Strong understanding of cybersecurity frameworks (eg, NIST, ISO 27001), secure software development, and encryption. Experience designing security for distributed systems, APIs, and AI/ML pipelines. Strong knowledge of cloud platforms (AWS, GCP, Azure), API security protocols, and DevSecOps practices. Proven experience in designing and reviewing modern application architectures in agile and data-driven environments. Familiarity with machine learning operations (MLOps), model governance and responsible AI frameworks. Excellent communicator, able to translate complex risks into actionable guidance. Industry certifications like CISSP, CCSP, or cloud security specializations.
