GDS Cyber - DPP - Staff - AI Data Protection Engineering
Ernst Young · Bengaluru · onsite · Posted 44d ago
Undisclosed5–10 yrsfulltimeSourced from
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About this role
Job 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.
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.
- 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.
- 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.
- 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
Sourced from naukri
Skills
Data lineageCyberclaudeapisai modelsamazon web servicesdata protectionclassificationcopilotcursorCrowdstrikeCapital MarketsCrowdStrike FalconcloudCloud SecurityCyber SecurityBusiness TransformationArchitectureMicrosoft AzureArtificial Intelligence
About Ernst & Young
Ernst Young
Management Consulting
44k+
employees
2002
24 years old
Gurgaon / Gurugram, Haryana
India
₹15.3L PA avg
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