Lead Software Engineer

Wells Fargo Bank•Irving, TX

10d•Hybrid

About The Position

Wells Fargo is seeking a Lead Python Software Engineer in the Secured Network Services organization with a goal to deliver automation solutions that enable a software driven Infrastructure environment. If you're passionate about automation, cloud-native technologies, and building scalable systems, this is your opportunity to make a significant impact in a large-scale enterprise environment. In this role, you will create API-driven integrations between network platforms and internal systems, develop and maintain automation frameworks that improve reliability and reduce operational toil, and translate manual operational procedures into codified, repeatable automation. You will ensure automation artifacts follow best practices for performance, maintainability, and security, and collaborate with network engineering and platform teams to align automation with broader architectural strategies. Additionally, you will lead the automation of product deployments, customer journeys, and the creation of APIs for our products and services. You will demonstrate proficiency in using AI-assisted development and analysis tools (e.g., GitHub Copilot and approved code-centric agents), leverage AI to accelerate system design, coding, testing, analysis, and troubleshooting, and apply strong technical judgment when validating and integrating AI-assisted outputs into solutions. You will understand and account for model limitations, security risks, and operational considerations, apply AI responsibly in development and production environments, and ensure AI usage aligns with security, compliance, privacy, and ethical standards.

Requirements

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

Nice To Haves

Proficiency in Python for building automation tooling, integrations, and workflow orchestration.
Experience with langchain/langraph, google ADK/A2A, AG-UI, and other common opensource libraries for developing AI workflows.
Experience with openai api spec and building interfaces into enterprise models.
Experience with MCP, Djando, graphQL, PSQL, no SQL, REACT, fast API, oAuth.
Experience with Multi agent orchestration.
Demonstrated ability to consume and build REST APIs, including authentication, schema validation, error handling, and versioning.
Hands-on experience with source control like GitHub.
Experience working in large‑scale, enterprise environments with change control and structured processes.
Extensive expertise in Microservices, RESTful API and event driven architectures.
Hands‑on experience with Ansible, including developing reusable playbooks, roles, and modules for network automation.
Proficiency in dealing with Legacy infrastructure along with cloud infrastructure (OpenShift, Cloud Foundry, Kubernetes).
Working knowledge of Unix, Linux, Mongo DB, Postman.

Responsibilities

Create API‑driven integrations between network platforms and internal systems.
Develop and maintain automation frameworks that improve reliability and reduce operational toil.
Translate manual operational procedures into codified, repeatable automation.
Ensure automation artifacts follow best practices for performance, maintainability, and security.
Collaborate with network engineering and platform teams to align automation with broader architectural strategies.
Lead the automation of product deployments, customer journeys, and creation of APIs for our products and services.
Demonstrate proficiency in using AI-assisted development and analysis tools (e.g., GitHub Copilot and approved code‑centric agents).
Leverage AI to accelerate system design, coding, testing, analysis, and troubleshooting.
Apply strong technical judgment when validating and integrating AI‑assisted outputs into solutions.
Understand and account for model limitations, security risks, and operational considerations.
Apply AI responsibly in development and production environments.
Ensure AI usage aligns with security, compliance, privacy, and ethical standards.