Lead Software Engineer - .NET Full-Stack Developer with Generative AI

About this role: Wells Fargo is seeking a Lead Software Engineer.

In this role, you will:

• Lead complex technology initiatives including those that are companywide with broad impact
• Act as a key participant in developing standards and companywide best practices for engineering complex and large scale technology solutions for technology engineering disciplines
• Design, code, test, debug, and document for projects and programs
• Review and analyze complex, large-scale technology solutions for tactical and strategic business objectives, enterprise technological environment, and technical challenges that require in-depth evaluation of multiple factors, including intangibles or unprecedented technical factors
• Make decisions in developing standard and companywide best practices for engineering and technology solutions requiring understanding of industry best practices and new technologies, influencing and leading technology team to meet deliverables and drive new initiatives
• Collaborate and consult with key technical experts, senior technology team, and external industry groups to resolve complex technical issues and achieve goals
• Lead projects, teams, or serve as a peer mentor

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

• 5+ years of Software Engineering experience
• Strong hands-on development expertise
• Experience delivering scalable applications
• Experience in technical leadership
• C#, .NET Core or Java Spring Boot, REACT or Angular JS, REST/Web APIs
• Microservices and distributed systems
• Cloud platforms (Azure preferred)
• Databases (SQL/NoSQL)
• Messaging and event-driven architecture
• Docker, Kubernetes
• OS knowledge (Windows/Linux)

AI Modern Engineering Skills:

• Experience leveraging AI-assisted development tools (e.g., GitHub Copilot, ChatGPT)
• Ability to guide teams in using AI tools to improve productivity and code quality
• Knowledge of AI/ML basics and LLM usage patterns
• Experience integrating AI capabilities using API/SDK-based services
• Understanding of responsible AI practices and enterprise compliance considerations
• Awareness of AI system operations, monitoring, and cost-performance trade-offs