Lead Software Engineer - Python

Wells Fargo•Irving, TX

3d•Hybrid

About The Position

Wells Fargo is seeking a Lead Software Engineer (Python) within the Secured Network Services organization. The role focuses on designing and delivering automation solutions that enable a software-driven infrastructure environment. The engineer will use Python development, automation, API integration, and cloud-native engineering to build scalable, reliable, and secure solutions. The role leads complex technology initiatives, including companywide initiatives with broad impact, and leads projects or teams or serves as a peer mentor. It involves developing API-driven integrations between network platforms and internal systems, maintaining Python automation frameworks, and transforming manual operations into repeatable workflows. The position partners with network engineering, platform, and technology teams to align solutions with wider architecture and infrastructure strategies. The engineer will also use approved AI-assisted development tools, including GitHub Copilot and code-focused AI agents, to support software engineering work. They will apply technical judgment when validating and integrating AI-assisted outputs into production solutions. This position offers a hybrid work schedule and is not eligible for telecommuting or visa sponsorship.

Requirements

5+ years of Software Engineering experience, or equivalent demonstrated through work experience, training, military experience, education, or a combination.
4+ years of Python development experience, including building automation tools, system integrations, APIs, or workflow orchestration solutions.
Experience designing, developing, testing, and supporting software solutions within an enterprise environment.
Experience developing and consuming REST APIs, including authentication, schema validation, error handling, and versioning.
Experience using source control and modern software development practices, preferably GitHub.

Nice To Haves

Experience with network engineering concepts and network infrastructure automation.
Experience with Nautobot for network source-of-truth, automation, or infrastructure management.
Experience developing API-driven integrations between network platforms and enterprise systems.
Experience with FastAPI, Django, GraphQL, PostgreSQL, NoSQL databases, React, and OAuth.
Experience with Model Context Protocol (MCP) and integrating applications with enterprise AI models or model APIs.
Experience working with OpenAI API specifications or similar API-based AI integrations.
Experience developing microservices, RESTful APIs, and event-driven architectures.
Experience with container and cloud-native platforms such as Red Hat OpenShift, Cloud Foundry, and Kubernetes.
Working knowledge of Unix/Linux, MongoDB, and Postman.
Experience working with legacy infrastructure as well as modern cloud and container-based infrastructure.
Experience working in large-scale enterprise environments with structured change management, security, risk, and release processes.
Experience using GitHub Copilot or other approved AI-assisted development tools for software design, coding, testing, troubleshooting, and analysis.
Ability to evaluate AI-generated code or recommendations for accuracy, maintainability, security, and operational risk.

Responsibilities

Lead complex technology initiatives, including companywide initiatives with broad impact.
Design and develop API-driven integrations between network platforms and internal systems.
Develop and maintain Python-based automation frameworks to improve reliability, scalability, and operational efficiency.
Transform manual operational processes into automated, repeatable, and maintainable workflows.
Design automation solutions that meet performance, security, reliability, and maintainability standards.
Partner with network engineering, platform, and technology teams to align automation solutions with broader architecture and infrastructure strategies.
Lead automation efforts supporting product deployments, service delivery, customer journeys, and API development.
Use approved AI-assisted development tools, including GitHub Copilot and code-focused AI agents, to support software engineering activities.
Leverage AI capabilities to accelerate solution design, development, testing, analysis, and troubleshooting.
Review, validate, and integrate AI-assisted outputs into production solutions using sound technical judgment.
Lead projects or teams, or serve as a peer mentor.