Staff Cloud Security Engineer

Sony Interactive Entertainment is a global leader in entertainment, producing the PlayStation family of products and services. The role of Staff Cloud Security Engineer involves defining and driving security architecture and strategy across multi-cloud environments, while leading the design and implementation of preventative security controls to address modern threats.

Responsibilities:

• Define and drive cloud security strategy, architecture standards, and technical roadmaps across cloud and AI-enabled environments
• Lead the design and implementation of preventative security controls, leveraging automation and AI-driven capabilities to reduce risk and improve detection and response
• Architect and secure complex multi-cloud and hybrid environments across AWS, Azure, GCP, and on-premise infrastructure
• Define and implement security architecture for AI/ML workloads, including model pipelines, data protection, and AI-integrated applications
• Identify and establish controls to mitigate AI-specific risks such as prompt injection, data poisoning, model leakage, and adversarial inputs
• Influence security and engineering practices across multiple teams and departments, driving adoption of secure-by-design principles
• Own the security outcomes of key cloud and AI initiatives, ensuring successful delivery and measurable risk reduction
• Establish and evolve DevSecOps and Infrastructure-as-Code (IaC) security standards, integrating security controls into CI/CD pipelines at scale
• Drive adoption and optimization of CNAPP platforms and related tooling to improve risk visibility and remediation across cloud, container, and AI environments
• Define and implement security architecture for containerized platforms (Kubernetes/EKS/GKE/AKS), including cluster hardening, workload isolation, image supply chain security, and runtime protection controls
• Lead the evolution of detection and response capabilities, integrating cloud telemetry, Cloud EDR, and advanced security analytics
• Conduct and guide threat modeling and risk assessments (Attack Surface Management, Data Security Posture Management, etc.) for complex cloud-native and AI-enabled systems
• Architect and deliver automation frameworks and security services to improve scalability and operational efficiency
• Provide technical leadership and mentorship to engineers, influencing department-level goals and technical direction

Requirements:

• Bachelor's or Master's degree in Computer Science, Information Security, or related field (or equivalent experience); typically 12+ years of relevant experience
• Proven experience defining and securing large-scale cloud and hybrid architectures (AWS, Azure, GCP, On-Premise)
• Deep expertise in cloud security architecture, including IAM, network segmentation, encryption, and secure design patterns
• Strong programming and automation experience, with the ability to design and scale security engineering solutions
• Extensive experience implementing DevSecOps practices and securing Infrastructure-as-Code (IaC) workflows
• Expertise working with container technologies (Kubernetes, Docker, EKS, GKE, AKS)
• Deep understanding of security risks in AI/ML systems, including prompt injection, data poisoning, model leakage, and adversarial inputs
• Experience defining and securing AI/ML architectures, including training pipelines, inference systems, and AI-integrated applications
• Strong knowledge of data security and privacy controls in AI systems
• Familiarity with frameworks such as OWASP Top 10 for LLMs and NIST AI Risk Management Framework
• Experience leading implementation and adoption of CNAPP platforms
• Deep understanding of cloud detection and response, IaC scanning, and runtime protection
• Strong background in security prevention, detection, and response strategy
• Experience designing highly scalable, resilient, and secure architectures across application, network, and data layers
• Experience influencing technical direction across multiple teams or departments
• Experience securing advanced AI patterns such as LLM integrations, APIs, MCPs, RAG pipelines, or model services
• Relevant certifications (e.g., ISC2, GIAC, AWS, Azure, GCP)