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Staff AI Data Protection Engineering
EYAI Data Protection Field Engineer at EY deploying and integrating AI-driven data protection solutions across global clients. Focused on securing sensitive data, troubleshooting issues, and cross-team collaboration.
Tech Stack
Tools & technologiesCloudCyber SecurityPythonTensorflow
About the role
Key responsibilities & impact- Configure, deploy, and support AI-enabled data protection capabilities across client environments, including data discovery, classification, DLP-aligned controls, PKI/KMS integrations, and information rights management patterns.
- Integrate data protection platforms with AI models, copilots, and enterprise workflows to help clients protect sensitive information used in prompts, retrieval sources, generated outputs, and broader AI use cases.
- Execute implementation, validation, testing, and troubleshooting tasks for client deployments, including configuration tuning, issue identification, root-cause analysis, and stabilization support.
- Support workshops, technical assessments, pilots, and proof-of-value activities by translating business and security requirements into practical engineering tasks.
- Contribute to reusable playbooks, deployment guides, code snippets, engineering templates, and configuration standards that improve repeatability across engagements.
- Work with cross-functional teams spanning cybersecurity, privacy, AI engineering, cloud, and client stakeholders to deliver secure and workable outcomes.
Requirements
What you’ll need- Up to 5 years of experience in one or more of the following areas: data protection, DLP, information protection, data discovery/classification, security engineering, cloud security, or related cybersecurity engineering domains.
- Working knowledge of data protection fundamentals, including data discovery and classification, DLP concepts, PKI & KMS, and information rights management.
- Practical familiarity with AI/ML concepts relevant to data protection, including machine learning, deep learning, NLP, RAG, AI-assisted prioritization, and model risk scoring.
- Experience with at least some of the following tools/platforms: 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, TensorFlow.
- Strong troubleshooting mindset, structured communication, and comfort working in client-facing delivery environments.
- Field engineering patterns from Microsoft and AI FDE patterns from the market both strongly emphasize technical depth plus customer communication.
Benefits
Comp & perks- Competitive
- Global team events
ATS Keywords
✓ Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills & Tools
Data DiscoveryData ClassificationSecurity EngineeringCloud SecurityPKIKMSMachine LearningDeep LearningNLPModel Risk Scoring
Soft Skills
Structured Communication