Principal Engineer - Generative AI for Cyber Security Architecture

Wells Fargo & Company•Minneapolis, MN

19h•Hybrid

About The Position

Wells Fargo is seeking a Principal Engineer in Technology as part of Cybersecurity. As a Principal Engineer, you will serve as a pragmatic software engineer and high-level architect responsible for the strategic implementation of generative AI across our cyber security ecosystem. This role requires a professional with a broad history of solving complex problems across a variety of industries. You will be the technical authority ensuring that AI-driven security initiatives are not just innovative proofs-of-concept, but robust, production-grade systems that scale across the entire institution.

Requirements

7+ years of Engineering experience, or equivalent demonstrated through one or a combination of the following: work experience, training, military experience, education
Advanced knowledge of software design, systems and database architecture, systems administration, stream processing, and large language models.
5+ years of experience in senior or lead roles across multiple high-stakes environments, consistently delivering value through technical leadership.
Multi-Industry Expertise: Demonstrated success in varied sectors, including building, managing, and securing infrastructure.
Scale and Performance: A proven track record of managing data and refactoring systems to support large-scale, high-concurrency usage.
Infrastructure Evolution: Extensive experience navigating the full evolutionary cycle of modern computing—from managing on-site hardware and co-located servers to architecting serverless and cloud-native solutions.
Full-Spectrum Development: The ability to act as an engineer for rapid product lifecycles while also leading large-scale enterprise integration projects for global clients.

Nice To Haves

Experience with leadership presentations, organizing complex concepts into easy to understand business narratives
M.S. in Computer Systems or B.S. with relevant experience.
Experience working with data and engineering teams to optimize reports, dashboards and ETL processes.
Hands on knowledge of reporting and analytical tools such as Tableau, PowerBI, Python, Alteryx etc., as well as JIRA, Github and the Microsoft Fabric ecosystem including Power Apps and Power Automate.
Knowledge of machine learning concepts is a plus.
Hands on experience leveraging Python, SQL, Cron, and other analytic enablement tools
Strong book-of-work management and organizational skills.
Experience supporting and working with cross-functional teams in a dynamic environment.
Experience leading risk projects to deliver complex data driven risk management solutions
Hands-on ability to manipulate data and tools to prototype and present solutions
Confident and self-motivated producer of original ideas and solutions, with sound judgment for when to escalate issues
Strong written and verbal communicator with excellent presentation skills

Responsibilities

Act as an advisor to leadership to develop or influence applications, network, information security, database, operating systems, or web technologies for highly complex business and technical needs across multiple groups
Lead the strategy and resolution of highly complex and unique challenges requiring in-depth evaluation across multiple areas or the enterprise, delivering solutions that are long-term, large-scale and require vision, creativity, innovation, advanced analytical and inductive thinking
Translate advanced technology experience, an in-depth knowledge of the organizations tactical and strategic business objectives, the enterprise technological environment, the organization structure, and strategic technological opportunities and requirements into technical engineering solutions
Provide vision, direction and expertise to leadership on implementing innovative and significant business solutions
Maintain knowledge of industry best practices and new technologies and recommends innovations that enhance operations or provide a competitive advantage to the organization
Strategically engage with all levels of professionals and managers across the enterprise and serve as an expert advisor to leadership
AI Lifecycle Orchestration: Lead the transition of critical AI components from experimental phases to high-availability production environments.
Architectural Stewardship: Oversee the design and development of system-wide integrations, emphasizing database and systems design, networking, and stream processing to ensure a cohesive security posture.
Operational Optimization: Leverage deep experience in managing massive data sets to reduce operational overhead, optimize infrastructure billing, and ensure long-term data transparency.
System Modernization: Direct large-scale refactoring efforts to move legacy or monolithic applications toward high-performance, API and Agent focused, and loosely coupled architectures.
Global Technical Mentorship: Promote high-quality engineering practices and mentor junior and senior-level staff to foster a culture of pragmatic, diverse technical knowledge.