About this role:
Wells Fargo is seeking a Senior Software Engineer
In this role, you will:
Lead or participate in complex initiatives on selected domains
Assure quality, security and compliance for supported systems and applications
Serve as a technical resource in finding software solutions
Review and evaluate user needs and determine requirements
Provide technical support, advice, and consultation with the issues relating to supported applications
Create test data and conduct interfaces and unit tests
Design, code, test, debug and document programs using Agile development practices
Understand and participate to ensure compliance and risk management requirements for supported area are met and work with other stakeholders to implement key risk initiatives
Conduct research and resolve problems in relation to processes and recommend solutions and process improvements
Assist other individuals in advanced software development
Collaborate and consult with peers, colleagues and managers to resolve issues and achieve goals
Required Qualifications:
4+ years of Specialty Software Engineering experience, or equivalent demonstrated through one or a combination of the following: work experience, training, military experience, education
Desired Qualifications:
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 automation solutions.
Mentor junior engineers and drive best practices in agentic system design, prompt engineering, and AI safety/guardrails.
Contribute to CI/CD pipelines, containerization, and deployment strategy for the platform.
Job Expectations:
10–15 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.
Key Responsibilities
Architect and build a Valence (CIBX) agentic platform that…