Join EY as an AI Data Protection Field Engineer and contribute to building a unique career. We offer global scale, support, and an inclusive culture to help you become your best. Your unique perspective is vital in helping EY improve. Build an exceptional experience for yourself and contribute to a better working world for everyone.

This role focuses on deploying, integrating, testing, and troubleshooting AI-enabled data protection solutions for global clients. It's a hands-on engineering position designed to secure sensitive data across cloud, SaaS, endpoint, collaboration, and AI environments by merging data protection fundamentals with practical AI engineering skills. Ideal for early to mid-career professionals who thrive on solving real client problems and developing technical expertise in modern data protection.

Key responsibilities include configuring and supporting AI-enabled data protection features like data discovery, classification, DLP controls, and PKI/KMS integrations. You will integrate data protection platforms with AI models, copilots, and enterprise workflows to safeguard sensitive information within AI use cases. This role involves executing implementation, validation, testing, and troubleshooting for client deployments, including configuration tuning and root-cause analysis. You will also contribute to reusable playbooks, guides, and templates to enhance engagement repeatability and collaborate with cross-functional teams.

We are looking for candidates with up to 5 years of experience in data protection, DLP, information protection, data discovery/classification, security engineering, or cloud security. A working knowledge of data protection fundamentals and practical familiarity with AI/ML concepts relevant to data protection are essential. Experience with tools such as Microsoft Copilot, GitHub Copilot, Cursor, VS Code with AI extensions, Claude Enterprise, Gemini Enterprise, Cyera, Varonis, Sentra, CrowdStrike Falcon DSPM, Wiz DSPM, Microsoft Purview, Python, or TensorFlow is required. A strong troubleshooting mindset and comfort in client-facing delivery environments are also key.