Join EY's Global Managed Services Transformation (GMx) initiative to reimagine managed service delivery. This role leads AI-driven modernization, leveraging cutting-edge technologies like Generative AI, Agentic AI, machine learning, and cloud-native engineering. The goal is to create future-ready, insight-driven service models that enhance operational outcomes and accelerate value for clients.

This is an opportunity to build a career at a global scale, with a supportive and inclusive culture. EY is committed to fostering a diverse environment where unique perspectives contribute to continuous improvement and the creation of a better working world.

Lead and architect AI-driven transformation initiatives within Managed Services, driving the end-to-end delivery of AI modernization modules. Engage in customer discussions to provide direction and information, acting as a technical lead for value-driven technology decisions. Translate client needs into actionable AI/automation use cases and solution roadmaps, identifying modernization opportunities through analytics and process re-engineering.

Mentor cross-functional teams, including AI engineers, ML engineers, and data specialists. Define governance, delivery frameworks, and quality standards, ensuring security, compliance, and operational stability across all AI and data platforms. Drive continuous improvement using AIOps and observability, championing an automation-first approach.

A minimum of 8-12 years in AI-driven technology delivery, with 3-6+ years in AI/ML leadership roles, is required. Essential expertise includes modern AI (Gen AI, Agentic AI), machine learning, deep learning, MLOps, automation, and cloud-native engineering, with a strong Python development background. Proven experience in architecting and delivering large-scale AI products in production cloud-native environments is crucial.

Demonstrated ability to quickly grasp new business processes and identify high ROI AI/ML transformation opportunities. Candidates must be adept at embedding security, responsible AI, and data governance into architectures, ensuring clear value realization. Experience with enterprise AI platforms, MLOps, AIOps, LLMOps, and supporting transformations in Managed Services environments is highly valued.