Principal Engineer — AI Data Platform

Wells Fargo•Irving, TX

23h•Hybrid

About The Position

COO Technology sits at the operational core of the enterprise, delivering the platforms that power how the Chief Operating Office runs at scale — from operational execution and regulatory enablement to customer experience, shared services, and supply chain. The Cognitive AI Solutions organization is building the next generation of agentic AI capabilities that turn enterprise process data into measurable operational outcomes. At the heart of this effort is a regulated, enterprise-grade, AI-ready data platform that serves as the persistent memory layer for our AI agents — unifying procedural knowledge, captured operational observations, organizational context, and risk and control data into a single governed substrate that powers process discovery, process reimagination, and downstream agent execution across the bank. The platform spans a hybrid-cloud footprint, moving data reliably between on-premise systems and public cloud environments to keep the enterprise's AI agents fed with trustworthy, well-governed data. We are seeking a Principal Engineer to serve as a senior technical leader on our AI data platform, driving the architecture and implementation of the pipelines and data quality capabilities that turn raw enterprise process data into a trusted, enterprise-grade fuel source for AI agents. You will own hard technical problems at the core of the platform — building scalable, production-grade data pipelines that move data between on-premise and cloud environments, instrumenting rigorous data quality and analytics capabilities, and applying Gen AI and agentic techniques to automate and scale data and analytics workflows. This is a deeply technical, hands-on role for an engineer who wants to shape the foundational, AI-ready data infrastructure behind agentic AI at enterprise scale.

Requirements

7+ years of Engineering experience, or equivalent demonstrated through one or a combination of the following: work experience, training, military experience, education
2+ years of experience building Gen AI and agentic solutions.
3+ years of experience building scalable, production-grade data pipelines and data quality analysis.
5+ years of experience with Python, with experience in ML frameworks.

Nice To Haves

Experience building or operating enterprise-grade, AI-ready data platforms in regulated industries (financial services, healthcare, or comparable).
Hands-on experience building data pipelines that move data between on-premise systems and public cloud environments.
Familiarity with vector databases, embedding models, and retrieval-augmented architectures.
Experience applying AI agents to data quality, data profiling, or analytics workflows.
Bachelor's degree in Computer Science, Machine Learning, Statistics, or a related quantitative discipline.

Responsibilities

Design and build scalable, production-grade data pipelines that reliably move and transform data between on-premise systems and public cloud environments, keeping the platform's AI-ready data stores fresh, complete, and trustworthy.
Build Gen AI and agentic solutions that automate data curation, enrichment, and analysis — using AI agents to accelerate data quality checks, anomaly detection, and insight generation across the platform.
Own data quality and analytics capabilities end to end — designing validation frameworks, monitoring, profiling, and analytics tooling that give the platform and its consumers confidence in the data.
Build production-grade data and ML pipelines in Python, leveraging modern ML and Gen AI frameworks for data transformation, enrichment, and agentic analytics at scale.
Own technical design across the ingestion-to-retrieval lifecycle — from on-prem and cloud ingestion through transformation, storage, and the gateway that serves governed, AI-ready data to downstream model applications.
Establish engineering rigor for the platform's data and AI-driven components — evaluation harnesses, data quality checks, drift monitoring, and reproducibility for pipelines and agentic analytics workflows.
Collaborate closely with data engineers, platform engineers, and applied AI engineers to ensure new pipeline, data quality, and agentic analytics capabilities integrate cleanly with the broader enterprise-grade data platform architecture.
Partner with the modeling and applied AI teams to ensure the data pipelines, quality signals, and agentic analytics you build translate directly into better agent performance in production.
Mentor engineers on the team in Gen AI and agentic solution design, scalable data pipeline engineering, and data quality and analytics best practices.
Contribute to architectural decisions and technical reviews, representing the data platform's pipeline, data quality, and agentic analytics capabilities in cross-team and leadership forums.