Generative AI Enterprise Architect

Our vision for the future is based on the idea that transforming financial lives starts by giving our people the freedom to transform their own. We have a flexible work environment, and fluid career paths. We not only encourage but celebrate internal mobility. We also recognize the importance of purpose, well-being, and work-life balance. Within Empower and our communities, we work hard to create a welcoming and inclusive environment, and our associates dedicate thousands of hours to volunteering for causes that matter most to them. Chart your own path and grow your career while helping more customers achieve financial freedom. Empower Yourself. ***Applicants must be authorized to work for any employer in the U.S. We are unable to sponsor or take over sponsorship of an employment visa at this time, including CPT/OPT.*** The   Generative AI   –   Enterprise Architect owns the architectural strategy and key decisions for how Generative AI capabilities are built, scaled, and integrated across the enterprise. This role defines how enterprise data and knowledge are structured, accessed, and applied to support AI-driven interactions across customer experiences, internal workflows, and decision-making processes.   This role is accountable for how information flows from source systems through curation, indexing, and retrieval into AI-enabled systems. It   establishes   the patterns, constraints, and standards that govern how AI   operates   within the enterprise, and ensures those patterns are applied consistently through close partnership with engineering and platform teams.   This role continuously evolves the architecture based on real-world system behavior, adoption patterns, and   emerging   capabilities, ensuring Generative AI becomes a reliable, scalable, and integral part of how the organization   operates .    What You Will Do   Own the architectural strategy and key decisions for how Generative AI capabilities are designed and scaled across the enterprise   Define how enterprise data, knowledge, and metadata are organized so AI systems can reliably access and use them   Establish how information flows from source systems through curation, indexing, embedding, and retrieval into AI-driven interactions   Set direction for how AI capabilities are embedded into customer touchpoints, internal tools, and operational workflows   Develop Agent Framework   Architecture   Define how enterprise data models and domain structures enable contextual reasoning and grounded outputs   Establish how knowledge is represented, versioned, and   maintained   to ensure accuracy and relevance over time   Define how retrieval systems select, rank, and deliver context to models under varying conditions   Establish patterns for handling non-deterministic system behavior, including variability, ambiguity, and failure scenarios   Define how orchestration layers coordinate models, data sources, APIs, and downstream systems into controlled workflows   Establish patterns for multi-step AI workflows, including tool usage, execution boundaries, and escalation paths   Assist the RAI team with d efin ing   how AI outputs are evaluated for quality, consistency, and impact on downstream decisions and processes   Identify   fragmentation across data, tools, and implementations and drive consolidation into coherent architectural patterns   What You Will Bring   Bachelor’s degree in Computer Science , Engineering, Data Science, or a related field   Ten or more years of experience across data architecture, distributed systems, or enterprise platform design   Experience owning architectural direction and influencing system design across multiple teams   Strong understanding of data architecture, including how structured and unstructured data is modeled, governed, and made accessible across complex systems    Experience designing large-scale systems that integrate data platforms, APIs, and distributed services   Practical knowledge of modern data platforms   ( such as snowflake )   including ingestion, transformation, and consumption layers   Understanding of retrieval, indexing, and search systems   operating   at scale   Deep   f amiliarity with Generative AI   and   Agentic AI   concepts including embeddings, retrieval, prompt composition,   agent memory,   and orchestration   Ability to design systems that balance performance, scalability, cost, and operational complexity   Strong grasp of access control, security, and governance within distributed environments   Experience with Agent Frameworks – 3 rd   Party and open source   Ability   to   direct   the   enterprise   on   prompt   injection risk, data leakage, least privilege, auditability, DLP, model risk controls, regulatory expectations, and safe use of agents connected to enterprise systems   Ability to translate complex system behavior into clear architectural direction   Strong decision-making skills with the ability to evaluate tradeoffs and set direction under uncertainty   What You Will Set You Apart   Experience designing systems where AI outputs directly drive user actions, decisions, or automated workflows   Depth in information retrieval, search systems, or large-scale knowledge architectures   Experience working with systems where output quality, consistency, and trust were critical and actively managed   Ability to design for ambiguity, variability, and imperfect data rather than assuming deterministic behavior   Strong intuition for how data structure, context selection, and system design impact model outputs   Track record   of simplifying complex, fragmented systems into clear and reusable architectural patterns   Experience   identifying   where AI is   appropriate   and where alternative approaches produce better outcomes   Practical understanding of how systems fail in production and how to design safeguards and fallback strategies