Senior AI Software Engineer

Wells Fargo Bank•Mclean, VA

1d•Hybrid

About The Position

Wells Fargo is seeking a Senior AI Software Engineer to design, build, and deliver AI-enabled applications and agentic systems. The role combines frontend engineering, Python backend services, MCP infrastructure, agent workflows, and secure enterprise integrations. The engineer will build user-facing applications and backend services integrating with Model Context Protocol (MCP) servers, LLM-based applications, RAG retrieval systems, and agent orchestration platforms, collaborating with AI developers, platform engineers, product teams, and architecture partners to deliver scalable, secure, observable, production-ready solutions. The position contributes across the full stack and helps establish reusable patterns for MCP-enabled tools, skills, and agent-driven workflows. It includes developing React and TypeScript applications; Python services, APIs, MCP services, and integration layers; MCP servers and clients; and multi-step agent workflows. The engineer will also build reusable components and tool schemas, integrate logging and observability, apply enterprise security and platform standards, and prepare services for deployment on OpenShift or Kubernetes. The position offers a hybrid work schedule and is located at 751 Pinnacle Dr., McLean, VA 22102. It is not eligible for visa sponsorship, and relocation assistance is not available. The listed base pay range is $143,000–$224,000. Employees may also be eligible for incentive opportunities. Posting end date: November 7, 2026; the posting may be removed earlier due to applicant volume.

Requirements

At least 4 years of engineering experience, or equivalent demonstrated through work experience, training, military experience, education, or a combination of these.
At least 3 years of Python proficiency and experience building services using frameworks such as FastMCP, FastAPI, and Flask.
At least 3 years of experience developing and integrating REST APIs, asynchronous services, and event-driven architectures.
At least 3 years of experience integrating with AI, LLM-based systems, agent platforms, or AI APIs.
At least 3 years of experience with React and TypeScript for frontend application development.
At least 3 years of experience with authentication, authorization, and access control patterns, including SSO, OAuth 2.0, API keys, token-based access, RBAC, and scopes.
At least 3 years of experience building or integrating with MCP servers and understanding the MCP protocol, including tool registration, schemas, message flows, clients, servers, and lifecycle patterns.
At least 3 years of experience developing agent-driven workflows with frameworks such as LangChain, Google ADK, or similar orchestration frameworks.
At least 3 years of experience with frontend fundamentals, including HTML, CSS, JavaScript, browser rendering, DOM APIs, and performance tuning.
At least 3 years of experience with automated testing using Playwright, performance testing, code reviews, and CI/CD practices.
At least 3 years of experience building secure, scalable, maintainable applications aligned with enterprise platform standards.

Nice To Haves

Experience building MCP servers using FastMCP or similar Python-based MCP frameworks.
Experience designing MCP-compatible tools, reusable skills, and modular agent capabilities.
Familiarity with tool schema design and structured output validation using Pydantic, JSON Schema, or Zod.
Experience with long-running autonomous agents supporting retries, state persistence, error recovery, graceful termination, and multi-step workflow execution.
Experience with RAG-based retrieval systems, vector search, embeddings, or enterprise knowledge retrieval patterns.
Experience with agent observability and tracing tools such as LangSmith, OpenTelemetry, Arize, or similar platforms.
Exposure to Splunk or enterprise observability frameworks.
Experience with Docker, OpenShift, Kubernetes, or similar containerization and deployment platforms.
Experience with Copilot Studio, Microsoft Teams UI integration, or conversational application platforms.
Knowledge of prompt engineering, skill engineering, and knowledge graphs such as llm_wiki and OKF.
Familiarity with secure enterprise software delivery, platform governance, and reusable engineering standards.

Responsibilities

Deliver end-to-end AI application features using MCP services and agent orchestration layers.
Own and contribute to MCP service implementations, including tool schema design, tool registration, API integration, authorization enforcement, and deployment readiness.
Build and maintain MCP-compatible tools, skills, APIs, and UI components for use across AI applications and agent runtimes.
Integrate AI-powered user interfaces with backend APIs, MCP servers, RAG systems, and agent workflows.
Implement RBAC policies governing which users, agents, clients, and applications can invoke specific tools or services.
Develop agent workflows that support complex user objectives through MCP and skill orchestration.
Align solutions with enterprise platform standards for application scaffolding, security, observability, logging, testing, and deployment.
Ensure frontend applications are performant, accessible, maintainable, and aligned with reusable design patterns.
Ensure backend and AI services are scalable, observable, secure, and ready for deployment on OpenShift or Kubernetes-based environments.
Write and maintain technical documentation, integration guides, API specifications, and operational runbooks.
Contribute to engineering best practices, including code reviews, automated testing, CI/CD, monitoring, and production support readiness.
Collaborate with AI developers, platform engineers, product teams, and architecture partners to deliver robust and scalable AI solutions.