Join EY and embark on a career journey where your unique perspective is valued. We are seeking a Manager, AI Data Protection Engineering, to lead client-facing data protection transformations at the crossroads of enterprise platforms, cybersecurity, and AI tools. This is a hands-on leadership role focused on architecting and implementing impactful solutions, guiding engineering teams, and influencing deal strategies. You will move beyond traditional delivery to architecting innovative solutions leveraging platforms and cutting-edge AI.

This role aligns with market demands for customer-embedded technical ownership and end-to-end production deployment, offering an exceptional opportunity to shape the future of data protection in an AI-driven world.

Lead the complete lifecycle of AI-enabled data protection solutions for key clients, from discovery and architecture to deployment. Translate complex business, privacy, and cybersecurity needs into robust, scalable technical designs. Act as a forward-deployed technical leader, working closely with clients to resolve ambiguous challenges, make critical architecture decisions, and drive tangible results in production environments.

Architect seamless integrations across diverse platforms including data protection tools, AI models, copilots, RAG pipelines, enterprise data estates, and downstream security controls. Manage engineering teams through build, integration, testing, and post-deployment phases, ensuring high-quality delivery, client trust, and efficient execution. Contribute to shaping new opportunities through compelling demonstrations, architectural designs, proofs of concept, and precise effort estimations. Develop reusable assets, reference architectures, and runbooks to enhance the firm's AI data protection capabilities and ensure consistent delivery across various sectors and accounts.

Effectively manage client expectations, project scope, solution quality, and technical risks. Provide technical leadership and guidance, enabling long-term client success through comprehensive documentation and knowledge transfer.

A minimum of 8 to 12 years of experience is required, encompassing areas such as data protection, cybersecurity engineering, security architecture, privacy engineering, and cloud security. Candidates must possess deep expertise in data protection, data discovery and classification, PKI & KMS, and information rights management. A strong working knowledge of ML, deep learning, NLP, RAG, AI-assisted prioritization, and model risk scoring is essential, with proven application in solving enterprise challenges.

Demonstrated success in designing and deploying enterprise-grade solutions across cloud and hybrid environments, ideally leveraging Azure, AWS, or GCP. Hands-on experience with AI/data protection tooling like Microsoft Copilot, GitHub Copilot, Claude Enterprise, Gemini Enterprise, Purview, DSPM platforms, and Python is crucial. Exceptional client-facing communication skills and the ability to serve as a trusted technical advisor to senior stakeholders are paramount.

Preferred qualifications include experience in professional services, solutions engineering, or a forward-deployed model. Familiarity with regulated industries such as financial services, healthcare, or the public sector is highly valued. Experience with pricing inputs, solution estimation, and technical deal shaping will also be advantageous.