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Job Summary EY GDS is seeking a highly motivated, results-driven, and quality-focused Agentic AI Generative AI Senior Manager to join our AI team. The ideal candidate will have deep experience in the Banking domain and a proven track record of architecting, leading, and scaling enterprise AI transformation programs. As an Agentic AI Gen AI Senior Manager, you will be responsible for shaping AI strategy, solution architecture, governance, and delivery of advanced AI solutions across banking. You will lead the adoption of next-generation Agentic AI frameworks, autonomous AI agents, AI orchestration patterns, Responsible AI, AI Governance, and Generative AI technologies, enabling intelligent automation, enhanced decision-making, regulatory alignment, and business transformation for clients. The role requires deep banking domain expertise, AI architecture leadership, strong Azure ecosystem knowledge, stakeholder management, and a passion for delivering impactful, secure, scalable, and responsible AI solutions. Your key responsibilities Lead enterprise strategy, architecture, and implementation of Agentic AI, Generative AI, Responsible AI, AI Governance, and advanced analytics solutions for Banking clients. Act as a Senior Manager, AI architect, and thought leader with strong business acumen, translating executive priorities and business requirements into scalable AI architectures and transformation roadmaps. Drive adoption of autonomous AI agents, multi-agent systems, agent orchestration frameworks, AI copilots, RAG / Agentic RAG solutions, and cloud-native GenAI platforms across banking processes. Partner with senior client stakeholders, architects, data scientists, AI engineers, product leaders, risk and compliance teams to identify, prioritize, and deliver high-value AI use cases. Own design authority for LLM-powered applications, enterprise AI agents, model evaluation, observability, LLMOps, AI security, Responsible AI controls, AI governance, and production deployment patterns. Develop Agentic AI and GenAI solutions for customer servicing, relationship management, lending, credit risk, fraud detection, KYC/AML, compliance monitoring, wealth advisory, investment research, portfolio intelligence, and operations transformation. Establish AI governance frameworks covering responsible AI principles, model risk management, explainability, fairness, privacy, security, auditability, regulatory compliance, and human-in-the-loop controls. Lead business development activities including client engagements, solution workshops, architecture reviews, proposal creation, RFP responses, executive presentations, and AI transformation roadmaps. Create reusable accelerators, reference architectures, delivery playbooks, and best-practice assets across Azure AI services, Agentic AI frameworks, and enterprise AI platforms. Stay current with advancements in Agentic AI, AI orchestration, LLMs, RAG, model evaluation, AI governance, autonomous workflows, Responsible AI, Azure AI ecosystem, and emerging cloud AI capabilities. Present business outcomes, technical architecture, risk considerations, insights, and recommendations to senior client stakeholders and leadership teams in a clear and impactful manner. Lead and manage multidisciplinary teams of AI architects, GenAI specialists, data scientists, engineers, and consultants, providing strategic direction and delivery oversight. Provide thought leadership on enterprise AI architecture, Agentic AI adoption, Responsible AI, AI Governance, cloud AI modernization, and GenAI operating models for Banking. Mentor and coach managers, junior architects, analysts, and developers, fostering technical excellence, innovation, engineering discipline, and responsible AI delivery practices. Skills and Attributes Professional Experience 15+ years of experience in Artificial Intelligence, Generative AI, Agentic AI, Advanced Analytics, AI architecture, and enterprise technology transformation, with a strong record of designing, building, governing, and scaling production AI solutions. At least 7+ years of experience leading AI-driven transformation programs within the Banking industry. Proven experience as an AI leader / AI architect, managing large-scale programs, cross-functional teams, solution architecture, client delivery, governance, and executive stakeholder engagement. Extensive experience identifying, designing, and implementing high-value AI use cases across retail banking, commercial banking, investment banking, wealth management, risk, compliance, fraud, operations, and customer experience domains. Educational Background Bachelor's or Master's degree in Computer Science, Data Science, Artificial Intelligence, Machine Learning, Engineering, Mathematics, Statistics, or a related quantitative field. Advanced certifications in Azure AI, Generative AI, Machine Learning, Solution Architecture, Cloud Technologies, Responsible AI, AI Governance, or Agentic AI platforms are preferred. Technical Skills Agentic AI Generative AI Expertise in Agentic AI, multi-agent systems, autonomous workflows, AI orchestration, RAG, Graph RAG, Agentic RAG, LLM fine-tuning, prompt engineering, AI governance, Responsible AI, knowledge graphs, AI copilots, and enterprise-scale LLM solutions. Microsoft Azure AI Ecosystem (Mandatory) Strong hands-on experience with Azure OpenAI, Azure AI Foundry, Azure AI Services, AI Search, Azure ML, Azure Databricks, AKS, Azure Functions, Azure Synapse, Data Factory, Microsoft Fabric, Azure DevOps, and cloud-native Azure architectures for enterprise AI solutions. Agentic AI Frameworks Experience building, orchestrating, deploying, governing, and monitoring AI agents and multi-agent systems using Microsoft Agent Framework, LangGraph, LangChain, CrewAI, and related orchestration platforms. AI Architecture, Responsible AI Governance Strong knowledge of enterprise AI architecture, AI governance operating models, Responsible AI frameworks, model risk management, explainability, fairness, privacy, security, compliance, auditability, and human-in-the-loop review patterns. AI/ML Data Science Strong expertise in Machine Learning, Deep Learning, NLP, Generative AI, Predictive Analytics, Computer Vision, Recommendation Systems, Time Series Forecasting, Statistical Modeling, model evaluation, and observability.
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