Generative AI Enterprise Architect
EmpowerAbout the role
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.***
We are looking for an Enterprise Data Architect to own how Generative AI capabilities are designed, structured, and scaled across the enterprise. This role defines how data, knowledge, and large language models (LLM) come together to power AI-driven interactions across customer experiences, internal tools, and business workflows.
You will shape how information flows across the organization and how AI systems access, interpret, and apply that information. This includes defining how structured data and unstructured knowledge are prepared, indexed, and retrieved to support reliable, high-quality outputs.
This is a strategic architecture role with direct influence on how systems are built. You will set architectural direction, make key design decisions, and guide engineering and platform teams to ensure a consistent and scalable approach.
What You Will Do
Define the architectural strategy for Generative AI, LLM, and AI platform capabilities across the enterprise
Shape how enterprise data, knowledge, and metadata are structured and accessed for AI-driven use cases
Establish patterns for how data flows across ingestion, transformation, indexing, embeddings, and retrieval
Guide how AI systems handle ambiguity, variability, and non-deterministic behavior
Influence how models, data sources, APIs, and workflows are orchestrated into scalable systems
Establish how AI systems are evaluated, observed, and operated in production, including output quality, system behavior, and reliability
What You Will Bring
Bachelor’s degree in Computer Science, Engineering, Data Science, or a related technical field
10+ years of experience in data architecture, AI/ML systems, or distributed platform design
Experience setting architectural direction and influencing system design across teams
Strong background in data modeling, data pipelines, and large-scale data systems
Understanding of retrieval, search, and indexing systems, along with Generative AI architectures including LLMs, embeddings, RAG, tool-calling agents, API integration, human-in-the-loop approvals, agent identity, multi-agent patterns, MCP and A2A-style integrations, and controlled autonomy
Experience with modern data and cloud platforms, especially AWS and Snowflake, including Bedrock, vector databases, hybrid search, APIs, event-driven architecture, IAM, observability, CI/CD, infrastructure as code, and model selection across LLMs, SLMs, and multimodal models
Ability to design systems that balance scalability, performance, cost, and operational complexity
What You Will Set You Apart
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