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Senior Lead Artificial Intelligence Solutions Consultant - Forward Deployed Agentic Engineer

https://www.wellsfargo.com/ Logo

Wells Fargo

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Location:
United States , Charlotte ▼

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Category:
IT - Software Development

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Contract Type:
Not provided

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Salary:

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Job Description:

Wells Fargo is seeking a Senior Lead Artificial Intelligence Solutions Consultant - 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.

Job Responsibility:

  • Forward Deployment of Agentic AI Products
  • Discovery, Process Intelligence & Value Realization
  • Agentic Solution Design, MCP Tools & Workflow Orchestration
  • LLM & GenAI Expertise (Hands-on Execution)
  • Capability Development: Agentic Skills Library & Reusable Assets

Requirements:

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

Additional Information:

Job Posted:
May 05, 2026

Expiration:
May 08, 2026

Employment Type:
Fulltime
Work Type:
Hybrid work
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