Lead AI Engineer

Wells Fargo & Company•Concord, CA

1d•$119,000 - $224,000•Hybrid

About The Position

At Wells Fargo, we are building enterprise‑grade Generative AI capabilities that are secure, scalable, and deeply integrated into our business workflows. The Lead AI Engineer plays a critical role in translating Generative AI strategy into production‑ready systems that operate at enterprise scale. This role sits at the intersection of hands‑on technical leadership, architectural decision‑making, and delivery accountability. You will lead the design and implementation of Retrieval‑Augmented Generation (RAG) pipelines, agentic AI orchestration, and shared GenAI services that power high‑visibility enterprise programs such as DASH and other priority initiatives. This is a senior individual contributor role with broad enterprise impact. You will influence GenAI architecture and delivery patterns across multiple teams and lines of business, while ensuring all solutions meet Wells Fargo’s non‑functional requirements, governance expectations, and risk standards. Technical Leadership & Architecture Lead the end‑to‑end design, development, and deployment of enterprise Generative AI solutions, including RAG pipelines and agentic AI systems. Own architectural decisions for GenAI orchestration, latency optimization, scalability, resilience, and cost efficiency across production environments. Define and enforce engineering best practices for GenAI development, including prompt design, evaluation frameworks, observability, and failure handling. Establish and evolve reusable GenAI patterns, reference architectures, and shared services to enable consistent, compliant solutions across the enterprise. Drive technical design reviews, architecture decision records (ADRs), and knowledge transfer to ensure long‑term maintainability and reuse. Delivery & Execution Lead moderately to highly complex initiatives with clear accountability for production outcomes—not just feature delivery. Partner with product, platform, risk, and governance teams to translate business needs into secure, compliant, production‑ready AI solutions. Ensure GenAI services meet enterprise non‑functional requirements, including latency, availability, scalability, auditability, and cost controls. Act as an escalation point for complex technical issues across GenAI platforms and embedded delivery teams. Collaboration & Stakeholder Management Collaborate closely with Forward Deployed Engineers (FDEs) and Line‑of‑Business teams to support embedded GenAI delivery models. Serve as a technical bridge between central GenAI platforms and consuming application teams. Partner with risk, compliance, and model governance stakeholders to proactively address concerns and enable timely production approvals. Communicate clearly with senior engineering leaders and stakeholders on technical tradeoffs, risks, and delivery status. People Leadership & Mentorship Provide technical mentorship to AI and software engineers, raising overall engineering maturity across teams. Review code, designs, and implementation approaches to ensure alignment with enterprise standards and best practices. Contribute to talent development, onboarding, and interview loops for AI engineering roles. This role does not include direct people management but requires strong technical influence and leadership.

Requirements

5+ years of software engineering experience, or equivalent demonstrated through work experience, training, military experience, or education.
2+ years of hands‑on experience building and deploying AI/ML solutions in production environments.
2+ years of experience working with Generative AI systems, including large language models.
2+ years of experience in Python and modern AI/ML frameworks (e.g., PyTorch, Transformers).

Nice To Haves

Proven experience designing and implementing Retrieval‑Augmented Generation (RAG) architectures using vector databases and retrieval frameworks.
Experience building agentic AI or multi‑agent orchestration systems.
Familiarity with enterprise AI governance, model risk management, and model lifecycle controls.
Experience supporting enterprise‑scale GenAI platforms or shared AI services.
Strong understanding of observability, monitoring, and performance optimization for AI workloads.
Prior experience working in highly regulated environments; financial services experience is a plus.
Experience deploying and operating AI solutions on cloud platforms (GCP, AWS, or Azure).
Solid understanding of distributed systems, APIs, and microservices architecture.
Demonstrated ability to lead technical initiatives and influence engineering decisions across multiple teams.

Responsibilities

Lead the end‑to‑end design, development, and deployment of enterprise Generative AI solutions, including RAG pipelines and agentic AI systems.
Own architectural decisions for GenAI orchestration, latency optimization, scalability, resilience, and cost efficiency across production environments.
Define and enforce engineering best practices for GenAI development, including prompt design, evaluation frameworks, observability, and failure handling.
Establish and evolve reusable GenAI patterns, reference architectures, and shared services to enable consistent, compliant solutions across the enterprise.
Drive technical design reviews, architecture decision records (ADRs), and knowledge transfer to ensure long‑term maintainability and reuse.
Lead moderately to highly complex initiatives with clear accountability for production outcomes—not just feature delivery.
Partner with product, platform, risk, and governance teams to translate business needs into secure, compliant, production‑ready AI solutions.
Ensure GenAI services meet enterprise non‑functional requirements, including latency, availability, scalability, auditability, and cost controls.
Act as an escalation point for complex technical issues across GenAI platforms and embedded delivery teams.
Collaborate closely with Forward Deployed Engineers (FDEs) and Line‑of‑Business teams to support embedded GenAI delivery models.
Serve as a technical bridge between central GenAI platforms and consuming application teams.
Partner with risk, compliance, and model governance stakeholders to proactively address concerns and enable timely production approvals.
Communicate clearly with senior engineering leaders and stakeholders on technical tradeoffs, risks, and delivery status.
Provide technical mentorship to AI and software engineers, raising overall engineering maturity across teams.
Review code, designs, and implementation approaches to ensure alignment with enterprise standards and best practices.
Contribute to talent development, onboarding, and interview loops for AI engineering roles.