Senior Lead Artificial Intelligence Solutions Consultant - Forward Deployed Agentic Engineer

• 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:

• 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:

• 7+ years of Artificial Intelligence Solutions experience, or equivalent demonstrated through one or a combination of the following: work experience, training, military experience, 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.

• 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, function schemas, multi‑agent coordination, planning loops, memory strategies, and guardrails.

• RAG, Context Injection & Knowledge Integration
• :•Practical experience implementing RAG pipelines, embeddings, retrieval strategies, document chunking, re‑ranking, and contextual grounding to reliably augment LLM outputs.

• LLM‑to‑Enterprise Integration
• :•Experience integrating LLM agents with enterprise systems using Python (APIs, databases, SaaS tools, internal services) via MCP or comparable LLM‑integration frameworks.

• Python
• ‑•

Based Agentic Systems Engineering:

• Advanced Python proficiency for building production‑grade agentic systems, including planning loops, state management, async execution, and modular service design. +
• Customer‑Facing LLM Solution Ownership
• :•Ability to work directly with customers and senior stakeholders to translate business processes into LLM‑powered agentic solutions, define success metrics, and deliver measurable impact.

• Job Expectations:

• + This position is
• NOT•eligible for Visa sponsorship.

• 3 days on-site & 2 remote) + The ability to travel up to 10% of the time.

• Posting Location:

• + Charlotte, NC + Dallas, TX + Irving, TX + Chandler, AZ _The Global Operations functions adhere to a location strategy; therefore, your candidacy may be determined based on your current location. Remote work locations are not available for these roles, so if you are not in a location listed on the posting, you must commit to self-relocation within an agreed upon timeframe._

• Posting End Date:

• 8 May 2026
• _•Job posting may come down early due to volume of applicants.

• We Value Equal Opportunity
• Wells Fargo is an equal opportunity employer.

Canada:

• Applicants with Disabilities
• To request a medical accommodation during the application or interview process, visit Disability Inclusion at Wells Fargo (https://www.wellsfargojobs.com/en/diversity/disability-inclusion/) .
• Drug and Alcohol Policy
• Wells Fargo maintains a drug free workplace. Please see our Drug and Alcohol Policy (https://www.wellsfargojobs.com/en/wells-fargo-drug-and-alcohol-policy) to learn more.

• Wells Fargo Recruitment and Hiring Requirements:

• a. Third-Party recordings are prohibited unless authorized by Wells Fargo. b. Wells Fargo requires you to directly represent your own experiences during the recruiting and hiring process.

• Req Number:

• R-541685