GDS Cyber - DPP - Staff - AI Data Protection Engineering

Ernst & Young Advisory Services Sdn Bhd; Pune District; Hybrid; INR 1,200,000 - 2,200,000; Full time

Job summary

EY’s GDS Cyber - DPP team in Pune is seeking an AI Data Protection Field Engineer to deploy, integrate, test, and troubleshoot AI-enabled data protection solutions for global clients. This hands-on role focuses on securing sensitive data across cloud, SaaS, endpoint, collaboration, and AI-enabled environments by combining data protection fundamentals with practical AI engineering skills.

You will work with cross-functional teams spanning cybersecurity, privacy, AI engineering, cloud, and client

Qualifications

• Up to 5 years of experience in 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 ML, DL, 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.

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.
• Contribute to reusable playbooks, deployment guides, code snippets, engineering templates, and configuration standards that improve repeatability across engagements.
• Work with cross-functional teams spanning cybersecurity, privacy, AI engineering, cloud, and client stakeholders to deliver secure and workable outcomes.

Skills

Data protection

DLP

Cloud security

AI/ML concepts

Troubleshooting

Client-facing

Python scripting

PKI/KMS

Information rights management

Cybersecurity engineering

Tools

Microsoft Copilot

GitHub Copilot

VS Code AI extensions

Claude Enterprise

Gemini Enterprise

Cyera

Varonis

Sentra

CrowdStrike DSPM

Wiz DSPM

Microsoft Purview

Python

TensorFlow

Job description

GDS Cyber - DPP - Staff - AI Data Protection Engineering

Location: Pune

Other locations: Anywhere in Country

At EY, you’ll have the chance to build a career as unique as you are, with the global scale, support, inclusive culture and technology to become the best version of you. And we’re counting on your unique voice and perspective to help EY become even better, too. Join us and build an exceptional experience for yourself, and a better working world for all.

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.

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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