GDS Cyber - DPP - Staff - AI Data Protection Engineering

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. External field/FDE patterns emphasize embedding closely with customers and iterating quickly based on feedback, which should be reflected in this role.

Contribute to reusable playbooks, deployment guides, code snippets, engineering templates, and configuration standards that improve repeatability across engagements. Reusable accelerators and reference implementations are a common requirement in comparable AI engineering roles.

Work with cross-functional teams spanning cybersecurity, privacy, AI engineering, cloud, and client stakeholders to deliver secure and workable outcomes.

Required qualifications

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.

Preferred qualifications

Exposure to Microsoft Purview, DSPM, CASB, sensitivity labeling, data lineage, encryption, or privacy engineering.

Experience writing small scripts, automations, or integrations in Python.

Familiarity with cloud-native deployments on Azure, AWS, or GCP, and basic understanding of APIs and enterprise integrations. Comparable AI delivery roles frequently require these capabilities.

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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