Senior Software Engineering Manager - Fraud Technology

Job Description

In an environment where fraud threat actors are continuously evolving their tactics and attacks are becoming more complex, we need to lead and deploy advanced fraud solutions that operate effectively while shaping the advancement of fraud strategies and solutions to safe-guard our customers, the company and its employees.

About this role:

Wells Fargo is on the hunt for a Senior Gen A.I. Leader, who can build and lead a cross-functional team of engineers and data scientists to build and operationalize innovative GenAI products and capabilities that can detect and predict fraud impacting the bank, its customers, and its ecosystem, or introduce better efficiency in our fraud and claims routines and workflows or accelerate new capability development through software engineering.

You will lead a team of talented engineers while staying close to the technical work. This role requires a balance of people management and hands-on technical leadership, ensuring the team builds high-quality, scalable solutions while maintaining a strong engineering culture.

You’ll mentor engineers, contribute to technical decisions, provide architectural guidance, and ensure the successful delivery of critical work. We look for leaders who foster a culture of ownership, accountability, and continuous learning. You should have a strong technical foundation, experience solving complex engineering challenges, and a track record of building and scaling high-performing engineering teams.

This senior manager will be responsible for:

• Managing fraud models, pattern detection capabilities, improving the performance of existing fraud detection products and developing innovative fraud solutions using GenAI

• Lead the design, development, and management of innovative fraud and claims GenAI solutions across various fraud vectors impacting Wells Fargo or efficiency opportunities

• Work with the Fraud AI platform and product leaders/business partners to operationalize these capabilities and realize benefits and efficiency gains

In this role, you will:

• Manage a team of software engineers, engineering leads, and data scientists

• Focus on delivering GenAI commitments aligned to enterprise strategic priorities around fraud and claims mgmt.

• Build support for strategies with business and technology leaders

• Guide development of actionable roadmaps and plans for usage and deployment of GenAI capabilities

• Identify GenAI opportunities and strategies for continuous improvement of software engineering and data science practices

• Provide oversight to software craftsmanship, security, availability, resilience, and scalability of solutions developed by the teams or third-party providers

• Set management guidelines and partner with stakeholders to implement key fraud & claims GenAI initiatives

• Develop strategies for hiring engineering and GenAI / data science talent

• Lead implementation of projects and encourage engineering innovation

• Collaborate and influence all levels of professionals including more experienced managers

• Lead team to achieve objectives

• Interface with external agencies, regulatory bodies, or industry forums

• Manage allocation of people and financial resources for Technology Strategic Leadership

• Develop and guide a culture of talent development to meet business objectives and strategy

Required Qualifications, US:

• 6+ years of Software Engineering experience, or equivalent demonstrated through one or a combination of the following: work experience, training, military experience, education

• 3+ years of management or leadership experience

Desired Qualifications:

• Experience in building and operationalizing GenAI capabilities

• M.S. or PhD in Computer Science (especially AI/ML), Statistics, Engineering, or related fields

• Knowledge and expertise of fraud management tools and techniques

• Experience in communicating to executive management team regarding the condition of the portfolios and applicable fraud management strategies

• Research publications in prominent AI/ML venues such as conference or journals

• Strong expertise or background in foundational model capabilities from OpenAI, Anthropic, Meta, or Google incl. building agentic systems through usage of MCP is preferred

• Strong expertise in core AI topics such as deep learning (DL), reinforcement learning (RL), planning, information representation and retrieval, g