GDS Cyber - DPP - Manager - AI Data Protection Engineering

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AI Data Protection Forward Deployed Engineer – Manager Role summary We are looking for an AI Data Protection Forward Deployed Engineer – Manager to act as the technical owner for client-facing data protection transformations that sit at the intersection of enterprise platforms, cybersecurity controls, and AI tools. This role is for a hands-on leader who can move from discovery to design to deployment, embedding closely with clients to architect and implement high-impact solutions while leading engineering teams and shaping deals. Your internal role matrix positions this level as the next step beyond field/staff engineering: not just delivery, but architecting solutions at the intersection of platforms and AI tools (agents and models). That aligns closely with external FDE market patterns focused on customer-embedded technical ownership and end-to-end production deployment.   Key responsibilities

Lead the discovery, architecture, and deployment of AI-enabled data protection solutions for strategic clients, translating business, privacy, and cybersecurity requirements into scalable technical designs. Serve as a forward deployed technical leader, embedding with client stakeholders to break down ambiguous problem statements, make architecture decisions, and drive measurable outcomes in production. External FDE role definitions from EY, Databricks, Palantir, and Deloitte all emphasize this embedded technical ownership model. Architect integrations across data protection platforms, AI models, copilots, RAG pipelines, enterprise data estates, and downstream security/policy controls. Lead engineering teams through build, integration, testing, troubleshooting, go-live, and post-deployment hardening while maintaining delivery quality, client confidence, and practical execution pace. Shape pursuit solutions through demonstrations, solution architecture, proofs of concept, effort estimates, and technical proposals. This is explicitly called out in external FDE manager patterns. Build reusable methods, accelerators, reference architectures, and runbooks that strengthen the firm’s AI data protection offerings and improve repeatability across sectors and accounts. Manage client expectations, delivery scope, solution quality, risks, and technical escalations while coaching teams and enabling long-term client capability through documentation and knowledge transfer.

8–12 years of experience across data protection, cybersecurity engineering, security architecture, privacy engineering, cloud security, or related fields. Strong depth in data protection, data discovery/classification, PKI & KMS, and information rights management. Strong working knowledge of ML, deep learning, NLP, RAG, AI-assisted prioritization, and model risk scoring, with experience applying these to enterprise problem-solving. Demonstrated experience designing and deploying enterprise-grade solutions across cloud and hybrid environments, ideally on Azure, AWS, or GCP. Comparable FDE manager roles consistently require cloud-native delivery, APIs, and production systems ownership. Hands-on experience with AI/data protection tooling such as Microsoft Copilot, GitHub Copilot, Claude Enterprise, Gemini Enterprise, Purview, DSPM platforms, Python, and related enterprise engineering tools. Strong client-facing communication and the ability to operate as a trusted technical advisor to senior stakeholders.

Experience in a professional services, solutions engineering, field engineering, or forward deployed model. Familiarity with regulated