Forward Deployed Principal AI Engineer

Wells Fargo•Concord, CA

2d•$159,000 - $305,000•Hybrid

About The Position

Wells Fargo is seeking a Forward-Deployed AI Engineer to support the design, development, and deployment of enterprise-scale Artificial Intelligence solutions. This role sits at the intersection of advanced AI platforms, enterprise technology, and business problem-solving. The engineer will work closely with business partners, product teams, and engineering organizations to translate complex and often ambiguous requirements into secure, scalable, and production-ready AI solutions that deliver measurable business outcomes. This is a hands-on, customer-embedded engineering role that requires strong technical depth, a product-oriented mindset, and the ability to operate effectively in a regulated enterprise environment. In This Role, You Will Partner with business stakeholders, product owners, and engineering teams to understand business problems, identify AI-enabled opportunities, and define end-to-end technical solutions. Design, prototype, and deliver AI-driven workflows, agents, copilots, and automations using large language models (LLMs) and enterprise AI services. Integrate AI capabilities with enterprise platforms and systems (e.g., ServiceNow, Salesforce, data platforms, internal services) using secure APIs and orchestration patterns. Rapidly iterate on prototypes and transition them into production-ready solutions that meet enterprise standards for reliability, scalability, and supportability. Act as a technical bridge between business, product, data, security, and engineering teams to ensure solutions are usable, compliant, and aligned with business objectives. Lead solution architecture and design activities, including: Prompt engineering and AI workflow design API integration and service orchestration Enterprise knowledge and data integration Security, privacy, risk, and governance considerations Own solutions across the full lifecycle—from concept and proof of value through production deployment and continuous improvement. Apply and promote best practices for responsible AI, including model risk management, data protection, and compliance with enterprise and regulatory requirements. Contribute to the development of reusable patterns, standards, and guidance to support scalable AI adoption across the enterprise.

Requirements

7+ years of software engineering experience, including designing and delivering production-grade systems.
7 + experience with API design, integration, and distributed systems in an enterprise environment.
2+ Years of Hands-on experience building and integrating AI or ML-powered applications, including experience with large language models (LLMs).
2+ Years of experience in Python programming language.

Nice To Haves

Demonstrated ability to translate ambiguous business requirements into well-architected technical solutions.
Strong communication skills with the ability to engage effectively with both technical and non-technical stakeholders.
Experience working with AI platforms and services such as OpenAI, Anthropic, or similar technologies.
Experience integrating AI solutions with enterprise platforms such as ServiceNow, Salesforce, or internal workflow systems.
Familiarity with enterprise security, privacy, governance, and risk management practices, particularly in regulated industries.
Experience delivering solutions in large, matrixed organizations with multiple stakeholders.
A product-minded approach with a focus on user experience, business value, and operational impact.

Responsibilities

Partner with business stakeholders, product owners, and engineering teams to understand business problems, identify AI-enabled opportunities, and define end-to-end technical solutions.
Design, prototype, and deliver AI-driven workflows, agents, copilots, and automations using large language models (LLMs) and enterprise AI services.
Integrate AI capabilities with enterprise platforms and systems (e.g., ServiceNow, Salesforce, data platforms, internal services) using secure APIs and orchestration patterns.
Rapidly iterate on prototypes and transition them into production-ready solutions that meet enterprise standards for reliability, scalability, and supportability.
Act as a technical bridge between business, product, data, security, and engineering teams to ensure solutions are usable, compliant, and aligned with business objectives.
Lead solution architecture and design activities, including: Prompt engineering and AI workflow design API integration and service orchestration Enterprise knowledge and data integration Security, privacy, risk, and governance considerations
Own solutions across the full lifecycle—from concept and proof of value through production deployment and continuous improvement.
Apply and promote best practices for responsible AI, including model risk management, data protection, and compliance with enterprise and regulatory requirements.
Contribute to the development of reusable patterns, standards, and guidance to support scalable AI adoption across the enterprise.