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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.
Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in data protection, including data discovery, classification, and DLP, while integrating AI models and workflows to enhance security measures. Proficient in troubleshooting and client engagement, ensuring effective communication and delivery of secure solutions.
Highest-signal resume keywords
Data ProtectionDLP ConceptsAI/ML ConceptsTroubleshooting MindsetClient-Facing Delivery
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
Data DiscoveryData ClassificationSecurity EngineeringCloud SecurityPKIKMSMachine LearningDeep LearningNLPModel Risk Scoring
Soft Skills
Structured Communication
Tools & Technologies
Microsoft CopilotGitHub CopilotCursorVS CodeClaude EnterpriseGemini EnterpriseCyeraVaronisSentraCrowdStrike Falcon DSPM
Industry Keywords
Information Rights ManagementCybersecurity EngineeringAI-Enabled Data ProtectionTechnical AssessmentsProof-of-Value Activities
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