Join EY's Global Managed Services team as a Senior Manager, AI Solution & Transformation Architect. This role is central to driving innovation in our Tech Managed Services offerings, focusing on AI-first solutions across data management, application management, and AI-as-a-Service. You will embed agentic workflows, automation, and AI-driven productivity to deliver scalable, outcome-based models that create significant client impact and long-term value.

Lead the end-to-end AI solutioning for large Tech Managed Service proposals (RFP/RFIs). Define AI-driven productivity commitments with supporting automation roadmaps and develop ROI/TCO-backed AI narratives. Create AI-integrated operating models, reimagine traditional TechOps with agentic possibilities, and drive AI-backed value engineering for commercial differentiation. Design comprehensive AI architectures involving LLMs, NLP, RAG, agent skills, knowledge graphs, vector databases, and predictive analytics. Collaborate in workshops, discovery sessions, and design reviews with deal teams. Develop engaging managed service proposal materials infused with client context. Conduct sector and tool-specific AI research, publishing thought leadership. Maintain and leverage EY.ai Tech MS assets and collaborate with global stakeholders. Partner closely with super region leaders, client partners, and leadership teams. Lead Proofs of Concept (POCs), Minimum Viable Products (MVPs), and rapid prototyping initiatives. Evaluate and integrate emerging AI capabilities, perform AI maturity assessments, and design responsible and agentic AI guardrails. Conduct FinOps analysis and AI service cost estimations.

A minimum of 15 years of combined experience in consulting, pre-sales, and managed services is essential. A Bachelor's degree in Engineering is required, complemented by a Post-graduation in AI & ML. Relevant certifications include Azure AI Engineer, AI Architect, SNOW Assist, SAP AI Architect, ITIL Foundation, SAFe, Certified Solution Architect, and Certified Enterprise Architect. Expertise in enterprise observability and AIOps landscapes, along with multiple agentic AI platforms and automation tools, is crucial. Strong proficiency in client storytelling, verbal presentations, and PowerPoint is expected. Experience in data engineering, including SQL, ETL, streaming, and data lakes, is necessary, as is an understanding of data engineering agents. Knowledge of enterprise security, identity, compliance, and governance in AI deployments is important. Direct experience in agentic solution design, build, and deployment, with a deep understanding of LLMs, NLP, GenAI, RAG, prompt engineering, and model tuning, is required. Previous experience in Technology Managed Service pre-sales and solutioning as a Solution Architect is a prerequisite. Experience with multiple SaaS tools and platforms, pursuit solutioning (RFP, RFI, RFQ), and a strong awareness of the Technology Managed Service competitive landscape are considered advantageous.