Senior Lead - Gen AI Solutions Consultant

About This Role Wells Fargo is seeking a Senior Lead as Forward Deployed Agentic Engineer that will drive the Agentic AI products across the Chief Operating Office, acting as the bridge between product development, operations, and AI engineering. The role focuses on embedding LLM-powered, Agentic workflows directly into enterprise processes, enabling measurable business outcomes, operational transformation, and scalable AI adoption.

In This Role, You Will

• 1) Forward Deployment of Agentic AI Products
• Lead end-to-end deployment of agentic AI solutions into live banking operations
• Embed AI agents directly into operational workflows for automation and decision support.
• Work closely with business and operations teams to move from concept to production.
• Ensure enterprise-grade deployment, scalability, and governance of AI solutions.
• 2) Discovery, Process Intelligence & Value Realization
• Conduct structured discovery sessions with business and operations stakeholders.
• Analyze process artifacts (SOPs, runbooks, workflows, video recordings, process mining outputs, etc.) to identify AI and automation opportunities.
• Translate operational processes into agentic workflows and solution blueprints.
• Define KPIs and quantify business benefits (cost reduction, cycle time, risk mitigation, productivity gains).
• Build ROI models and value realization frameworks for AI initiatives.
• 3) Agentic Solution Design, MCP Tools & Workflow Orchestration
• Design and implement agentic AI solutions using process artifacts as input for workflow automation.
• Develop multi-step AI agents capable of reasoning, tool use, and task execution.
• Leverage MCP (Model Context Protocol) tools to integrate LLMs with enterprise systems, APIs, and data sources.
• Enable tool-augmented LLM workflows including function calling, orchestration, and structured outputs.
• Build reusable agent patterns for banking operations use cases (e.g., case resolution, reconciliation, compliance checks).
• 4). LLM & GenAI Expertise (Hands-on Execution)
• Hands-on experience with LLM platforms including:
• Claude (Anthropic)
• Gemini (Google)
• GPT (OpenAI)
• Design prompt architectures, reasoning chains, and agent workflows.
• Implement RAG (Retrieval-Augmented Generation) and contextual grounding strategies.
• Evaluate model performance, reliability, and enterprise readiness.
• Ensure responsible AI usage, governance, and compliance adherence.
• 5) Capability Development: Agentic Skills Library & Reusable Assets
• Build and maintain an Agentic Skills Library of reusable capabilities, including: Task-specific AI agents (e.g., summarization, reconciliation, classification, decision support).
• Workflow components (e.g., data extraction, validation, routing, reasoning chains).
• Prompt templates, tool-use patterns, and orchestration blueprints.
• Enable systematic reuse of agentic capabilities across multiple banking use cases.
• Establish standards for versioning, governance, and lifecycle management of agent skills.
• Continuously expand the library based on production learnings and new use cases.
• Drive “build once, reuse everywhere” approach for agentic automation.
• Required Qualifications:
• Overall 18+ of industry experience with 7+ years of Artificial Intelligence Solutions experience, or equivalent demonstrated through one or a combination of the following: work experience, training, military experience, education

**Required Qualifications for Europe, Middle East & Africa only:**

• Digital product management experience, or equivalent demonstrated through one or a combination of the following: work experience, training, education

**Desired Qualifications:**

• Production-Grade LLM Agent Deployment: Proven experience designing, deploying, and operating LLM-driven agentic systems in production, with attention to reliability, safety, evaluation, and governance in enterprise environments.
• Deep LLM Platform Expertise: Hands-on mastery of modern LLMs (GPT-4/4.1, Claude, Gemini, or equivalents), including model selection, prompt-to-model matching, latency/cost optimization, and comparative evaluation.
• LLM Reasoning & Prompt Engineering: Advanced capability in prompt engineering, structured reasoning, chain-of-thought management, constrained generation, and output validation for real-world workflows.
• Agentic LLM Architecture & Orchestration: Strong understanding of LLM-based agent architectures, including tool calling, functi…