Lead Software Engineer - Python/Gen AI/RAG

Job Description 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
• Lead projects, teams, or serve as a peer mentor

Required Qualifications:

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

Desired Qualifications:

• 5 - 10+ years of overall software engineering experience, with strong hands-on Python expertise.
• Proven experience building AI/Generative AI (GenAI) applications in production environments.
• Strong understanding and hands-on experience with RAG (Retrieval-Augmented Generation) architectures - embeddings, vector databases, chunking strategies, retrieval optimization.
• Practical experience building Agentic AI systems - multi-agent orchestration, planning, tool-calling, memory management (e.g., LangChain, LangGraph, AutoGen, CrewAI, DeepAgents, or similar).
• Experience designing low-code/configuration-driven frameworks or platforms for non-technical users.
• Solid understanding of LLM integration patterns (prompt engineering, function/tool calling, streaming responses via SSE/WebSockets).
• Strong API design skills using frameworks like FastAPI/Flask, with experience in asynchronous programming (asyncio).
• Experience with structured logging, observability, and resilience patterns (circuit breakers, retries) in distributed systems.
• Strong problem-solving skills and ability to work in a fast-paced, evolving AI product environment
• Experience with OpenShift/Kubernetes for container orchestration and platform deployment.
• Familiarity with MongoDB or other NoSQL databases for state/checkpoint management.
• Exposure to enterprise authentication/authorization patterns (AD/SSO, RBAC).
• Experience with CI/CD tooling (GitHub Actions, Jenkins) and code quality tools (SonarQube).
• Prior experience in financial services / capital markets domain.
• Familiarity with MCP (Model Context Protocol) or similar tool-integration standards.

Job Expectations:

• Architect and build a Valence (CIBX) agentic platform that allows business users to configure, deploy, and manage AI agents without deep technical expertise.
• Design and develop deep agents capable of multi-step planning, tool use, memory, and autonomous task execution (e.g., using frameworks like LangGraph, DeepAgents SDK, or similar).
• Build and optimize Retrieval-Augmented Generation (RAG) pipelines for grounding agent responses in enterprise knowledge bases and documents.
• Integrate Generative AI / LLM capabilities (via internal or third-party model gateways) into scalable backend services.
• Design orchestration layers for routing, planning, task execution, and result aggregation across multiple specialized agents.
• Enable business teams to build agents for automation, analytics, insights, and predictive use cases with reusable templates and configuration-driven workflows.
• Define and implement MCP (Model Context Protocol) or similar tool-calling protocols to connect agents with enterprise systems and APIs.
• Ensure platform scalability, reliability, and observability (logging, monitoring, tracing) for production-grade agentic workloads.
• Collaborate with product, architecture, and business stakeholders to translate BAU pain points into agent-driven automatio…