GDS Cyber DPP Staff AI Data Protection Engineering

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AI Data Protection Field Engineer

Role summary

We are looking for an AI Data Protection Field Engineer to help deploy integrate test and troubleshoot AI-enabled data protection solutions for global clients. This is a hands-on engineering role focused on helping clients secure sensitive data across cloud SaaS endpoint collaboration and AI-enabled environments by combining strong data protection fundamentals with practical AI engineering skills. The role is designed for professionals with early-career to mid-career experience who enjoy solving real client problems in delivery settings and building technical depth in modern data protection. The internal role design emphasizes deployment model/platform integration testing and troubleshooting while comparable market roles emphasize customer-facing engineering rapid iteration and production-grade solution delivery.

Key responsibilities

• 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. 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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