Staff Cloud Security Engineer

Why Sony Interactive Entertainment? Sony Interactive Entertainment isn’t just the Best Place to Play — it’s also the Best Place to Work. As a subsidiary of Sony Group Corporation, we’re part of a proud legacy of innovation and excellence. SIE is a dynamic technology company, delivering cutting‑edge hardware and network services to more than 100 million people and an entertainment leader, home to some of the most beloved and recognizable intellectual properties (IP) in the world.

Role Description

Provide cloud security capabilities that are proactive, preventive‑focused models that address modern threats, including those driven by AI‑enabled attack techniques. Expanding into next‑generation security domains such as AI/ML security, container security, and advanced threat detection and response. We are seeking a highly experienced Cloud Security Engineer (Staff) to define and drive security architecture, strategy, and engineering practices across multi‑cloud and hybrid environments. This role will serve as a technical leader responsible for securing cloud‑native and AI‑driven systems at scale while influencing security outcomes across multiple teams and departments.

Key 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.

Qualifications

• Bachelor’s degree or equivalent in Computer Science, Information Security, or related field.
• 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 securing advanced AI patterns such as LLM integrations, APIs, MCPs, RAG pipelines, or model services (preferred).