GDS Cyber-DPP-Senior-AI Data Protection Engineering

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AI Data Protection Staff Engineer

Role summary

We are looking for an AI Data Protection Staff Engineer to lead more complex engineering work across AI-enabled data protection deployments with a stronger emphasis on automation reusable assets and hybrid/agentic deployment patterns. This role builds on the field engineer foundation by adding greater solution depth stronger technical ownership and the ability to operationalize scalable patterns that can be reused across multiple client environments. The internal role matrix specifically places this level at above field engineer agentic hybrid deployment automate.

Key responsibilities

• Lead the engineering design and implementation of advanced data protection solutions spanning discovery/classification protection controls PKI/KMS integrations and rights management capabilities in AI-enabled enterprise environments.
• Build and industrialize automation for deployment policy tuning control validation reporting and operational workflows using scripting APIs and engineering tooling. The internal frontier AI materials also identify automation-oriented assets such as smart classification and data map rapid policy config deployer and triage orchestrator as target capabilities for the adaptive data protection pillar.
• Design and deploy hybrid and agentic patterns that bring together data protection platforms AI tools and enterprise data/workflow integrations while maintaining security privacy and control effectiveness.
• Own technical workstreams in client engagements including design decisions engineering quality integration approaches troubleshooting of complex issues and stabilization planning.
• Create reusable accelerators such as prompts engineering patterns code libraries configuration baselines implementation templates and testing artifacts to improve delivery speed and consistency across the practice. Comparable FDE and AI engineering roles explicitly emphasize reusable frameworks and reference implementations.
• Mentor junior engineers and help uplift practice capability through code reviews knowledge transfer technical coaching and contribution to internal engineering standards.
• Work across data protection AI engineering privacy and cloud teams to ensure solutions are operationally sound scalable and aligned with client architecture and compliance requirements.

Required qualifications

• 58 years of experience in data protection engineering cybersecurity engineering privacy engineering cloud security engineering or adjacent domains.
• Strong hands-on experience with data protection data discovery/classification PKI & KMS and information rights management.
• Working knowledge of ML deep learning NLP RAG AI-assisted prioritization and model risk scoring with the ability to apply these concepts in production-oriented delivery contexts.
• Experience with one or more major data protection ecosystems such as Microsoft Purview Cyera Varonis Sentra CrowdStrike Falcon DSPM or Wiz DSPM.
• Strong scripting/integration skills in Python plus experience with APIs cloud services and engineering toolchains.
• Ability to independently lead technical workstreams in ambiguous client-facing delivery environments. Comparable customer-facing AI engineering roles strongly emphasize production-grade delivery communication and end-to-end ownership.

Preferred qualifications

• Experience with automated policy deployment classification engineering data mapping or AI-enabled triage/orchestration patterns.
• Familiarity with privacy and cross-border data transfer considerations in AI use cases. Internal guidance explicitly highlights the importance of permissions personal data handling and cross-border processing when AI systems use enterprise data.
• Experience contributing to internal assets managed services or engineering standards within a professional services or product engineering environment.

Why join us

Join a globally connected cybersecurity practice helping clients protect sensitive data in an AI-driven world. You will work at the intersection of data protection privacy cloud and frontier AI helping shape practical scalable solutions that reduce risk enable trust and support secure business transformation. This is consistent with the internal positioning of the data protection practice as technology-enabled consulting-led and globally delivered.

Typical work environment

• Global cross-functional teams
• Mix of advisory architecture engineering and delivery
• Exposure to strategic client programs and market-shaping offerings
• Opportunity to build reusable assets accelerators and modernization patterns consistent with a global delivery model

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