GDS Cyber-DPP-Manager-AI Data Protection Engineering

EY; Dadri; On-site; INR 3,500,000 - 7,000,000; Full time

Job summary

EY is seeking an AI Data Protection Forward Deployed Engineer – Manager to act as the technical owner for client-facing data protection transformations at the intersection of enterprise platforms, cybersecurity controls, and AI tools. This hands-on leader moves from discovery to design to deployment, embedding with clients to architect high-impact solutions while guiding engineering teams.

You will mentor teams, shape deals, and build reusable assets while translating business and privacy

Qualifications

• 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.
• Experience designing and deploying enterprise-grade solutions across cloud and hybrid environments (Azure, AWS, GCP).
• Hands-on experience with AI/data protection tooling such as Microsoft Copilot, GitHub Copilot, Claude Enterprise, Gemini Enterprise, Purview, DSPM platforms, Python.
• Strong client-facing communication and ability to act as trusted technical advisor to senior stakeholders.

Responsibilities

• Lead discovery, architecture, and deployment of AI-enabled data protection solutions for strategic clients.
• Embed with client stakeholders to break down ambiguous problems, make architecture decisions, and drive measurable outcomes in production.
• Architect integrations across data protection platforms, AI models, copilots, RAG pipelines, and security controls.
• Lead engineering teams through build, integration, testing, go-live, and post-deployment hardening.
• Shape pursuit solutions through demonstrations, architecture, proofs of concept, and technical proposals.
• Build reusable methods, accelerators, reference architectures, and runbooks for AI data protection offerings.
• Manage client expectations, delivery scope, risks, and escalations while coaching teams.

Skills

Data protection

Cybersecurity engineering

Security architecture

Privacy engineering

Cloud security

Enterprise security

Tools

Microsoft Copilot

GitHub Copilot

Claude Enterprise

Gemini Enterprise

Purview

DSPM platforms

Python

Job description

GDS Cyber-DPP-Manager-AI Data Protection Engineering

Location: Noida

Other locations: Anywhere in Country

Date: Oct 23, 2026

Requisition ID: 1723806

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 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.

Required qualifications

• 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.

Preferred qualifications

• Experience in a professional services, solutions engineering, field engineering, or forward deployed model.
• Familiarity with regulated sectors such as financial services, healthcare, public sector, or large multinational enterprises. External senior digital trust/privacy FDE roles explicitly value regulated‑industry depth.
• Experience with pricing inputs, solution estimation, and technical deal shaping.

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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