Senior Specialty AI Engineer

Wells Fargo

Hybrid

Quick summary

Work type: Hybrid
Location: Charlotte, NC
Posted: 6 days ago
Closes: Jun 23, 2026
Nearby: 99+ roles within 25 mi

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Salary context

How this pay compares to similar roles

Similar $207k

$158k most similar roles pay here $265k

This listing doesn't post a salary. Most similar roles pay $168,593–$246,150.

Based on 240 similar postings.

Employer

About Wells Fargo

Wells Fargo & Company is one of the largest banks in the United States, providing banking, investment, mortgage, and consumer and commercial finance products and services nationwide. Industry: Banking & Financial Services

Wells Fargo currently has 87 open roles on FindRole.

Listed pay typically runs $144,009–$224,000 across 46 roles with salary data.

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At a glance

TL;DR · Senior Specialty AI Engineer

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Wells Fargo seeks a Senior Specialty AI Engineer to join its cutting-edge team, focusing on designing and deploying GenAI applications using LangChain/LangGraph for agentic workflows and RAG pipelines. This role involves developing multi-step workflows with error handling, building ingestion pipelines for document parsing and retrieval strategies like dense search and hybrid retrieval, and configuring vector databases such as Pinecone or Weaviate. Additionally, the engineer will work on deploying services to Google Vertex AI, contributing to full-stack development using Python (FastAPI) or Node.js for backend and React/Next.js for frontend, while ensuring security and observability. The ideal candidate has 4+ years of experience in AI/ML engineering, hands-on with LangChain and RAG pipelines, and proficiency in Docker, Kubernetes, and CI/CD workflows.

Skills

LangChain Google Vertex AI Python FastAPI React Next.js Docker Kubernetes CI/CD Pinecone Weaviate FAISS Node.js LlamaIndex Cohere OpenTelemetry GenAI evaluation concepts security fundamentals

What you'll do

Develop multi-step workflows using LangChain and LangGraph with error handling.
Build ingestion pipelines including document parsing, chunking, embeddings, and metadata tagging.
Configure vector databases for dense search and hybrid retrieval strategies in RAG pipelines.
Deploy services on Google Vertex AI, including model endpoints and vector search capabilities.
Implement backend APIs using Python (FastAPI) or Node.js and frontend components with React/Next.js.
Contribute to CI/CD workflows and containerization efforts via Kubernetes/GKE.

What we're looking for

4+ years of AI/ML software engineering experience
Hands-on experience with LangChain and familiarity with vector databases
Experience building RAG pipelines including chunking, embeddings, retrieval
Backend development skills in Python (FastAPI) or Node.js
Frontend expertise with React or Next.js
Knowledge of Docker, Kubernetes basics, and CI/CD pipelines
Understanding of GenAI evaluation concepts and security fundamentals

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