AI Data Protection Field Engineer

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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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