Senior AI Software Engineer

Wells Fargo•Tysons, VA

1d•$143,000 - $224,000•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 spans frontend applications, Python backend services, Model Context Protocol (MCP) infrastructure, agent workflows, and secure enterprise integrations. The engineer will build user-facing applications and backend services integrating with MCP servers, LLM-based applications, RAG retrieval systems, and agent orchestration platforms, collaborating with AI developers, platform engineers, product teams, and other stakeholders to deliver scalable, secure, observable, production-ready solutions. The position also contributes reusable patterns for MCP-enabled tools, skills, and agent-driven workflows. The role offers a hybrid work schedule at the listed McLean, Virginia job location. It is not eligible for visa sponsorship, and relocation assistance is not available. The posted base pay range is $143,000 to $224,000; pay may vary based on factors such as performance, skills, experience, and work location. Employees may also be eligible for incentive opportunities. Wells Fargo provides eligible employees a comprehensive benefits package.

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 strong Python proficiency and experience building services with frameworks such as FastMCP, FastAPI, or 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 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 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.
Must directly represent their own experience during the recruiting and hiring process and be prepared to provide accurate information and supporting documentation for lawful employment and education verification.
This position is not eligible for visa sponsorship.

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 that support 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

Design, build, and maintain AI-enabled applications using React, TypeScript, Python, and modern API frameworks.
Develop frontend applications aligned with enterprise UI standards and reusable component patterns.
Build and maintain Python backend services, APIs, MCP services, and integration layers using frameworks such as FastMCP, FastAPI, and Flask.
Design and integrate MCP servers and clients to enable secure context sharing between models, tools, applications, and agent runtimes.
Develop and support agentic workflows for complex, multi-step tasks using frameworks such as LangChain Deep Agents, Google ADK, or similar frameworks.
Build modular, skill-based agents with reusable, composable, and versioned skills.
Create reusable components, services, tool schemas, and integration patterns for enterprise AI applications.
Integrate logging, tracing, and observability frameworks, including Arize- and Splunk-compatible logging and agent observability tooling.
Prepare applications and services for deployment on enterprise container platforms such as OpenShift or Kubernetes.
Collaborate with AI developers, platform engineers, product teams, and architecture partners to deliver robust and scalable AI solutions.
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 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 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.
Follow applicable Wells Fargo policies and procedures and fulfill risk and compliance obligations, including proactive monitoring, governance, risk identification, escalation, and sound risk decisions.