Senior AI Engineer - Generative AI & Intelligent Automation

Wells Fargo is seeking a Senior AI Engineer to design and deliver enterprise-scale Generative AI, agentic systems, and intelligent document processing capabilities for Commercial Banking and Corporate & Investment Banking operations. This contract role is based in Charlotte, NC (hybrid) and focuses on building production-grade AI solutions that automate complex customer requests and operational workflows.

Key Responsibilities

• Design, develop, and deploy enterprise-grade Generative AI applications and intelligent automation solutions.
• Build and maintain AI agents using Python, LangGraph, LangChain, Google ADK, and agent-based architectures.
• Develop and optimize RAG (Retrieval-Augmented Generation) solutions for document understanding and knowledge retrieval.
• Create intelligent document processing workflows that extract, validate, and enrich information across multiple document types.
• Implement capabilities including OCR, PDF parsing, semantic matching, fuzzy comparison, confidence scoring, and rule-based validation.
• Develop API-driven integrations with internal platforms and operational systems.
• Build agent-driven workflows that:
  • Interpret customer intent using ML models.
  • Gather information from multiple enterprise applications.
  • Validate customer identities and business data.
  • Execute automated business processes.
  • Support KYC and compliance-related workflows.
  • Automatically resolve and close operational requests.
• Collaborate with product owners, architects, business stakeholders, and engineering teams to deliver scalable AI solutions.
• Ensure solutions are scalable, secure, highly available, and production-ready in a containerized cloud environment.

Required Qualifications

• Authorization to work for ANY employer in the U.S. This position is not eligible for visa sponsorship.
• 3+ years of experience delivering solutions in Artificial Intelligence, Machine Learning, Generative AI, Intelligent Automation, or related fields.
• 3+ years of hands-on Python development experience.
• Strong experience with LangGraph and LangChain.
• Strong experience with Google ADK.
• Strong experience with agent-based architectures.
• Strong experience with AI workflow orchestration.
• Proven experience designing and deploying LLM-powered applications.
• Proven experience designing and deploying AI Agents.
• Proven experience designing and deploying RAG solutions.
• Proven experience designing and deploying intelligent automation platforms.
• Extensive experience implementing Document AI / Intelligent Document Processing (IDP) including:
  • OCR
  • PDF parsing
  • Document extraction
  • Data classification
  • Confidence scoring
  • Rule-based validation
  • Semantic matching
  • Fuzzy comparison
  • API integrations
• Experience building and consuming REST APIs.
• Hands-on experience working with cloud-based AI and GenAI services.
• Experience working within highly containerized environments.
• Strong analytical and problem-solving skills.
• Excellent communication and collaboration skills.

Technical Environment

• Python
• LangGraph
• LangChain
• Google ADK
• Generative AI / LLM Platforms
• Retrieval-Augmented Generation (RAG)
• MongoDB
• Kafka
• REST APIs
• Java Services
• OCR & Document Extraction Technologies
• Cloud-Native AI Services
• Containerized Microservices Architecture

Preferred Qualifications

• Experience within financial services, banking, or highly regulated industries.
• Experience with MongoDB.
• Experience with Kafka.
• Experience with Java-based services and APIs.
• Experience with Kubernetes and container orchestration platforms.
• Knowledge of KYC, customer onboarding, compliance, or operational process automation.
• Experience integrating AI solutions with enterprise workflow systems.

Benefits

• Health Insurance
• Life insurance
• 401K
• Voluntary Benefits

What Will Help You Succeed

• Real-world implementation experience building AI-powered solutions in production environments.
• Experience building and deploying RAG solutions.
• Experience developing AI agents using LangGraph and Python.
• Track record of automating document-processing workflows.
• Ability to extract and validate data from unstructured documents.
• Experience integrating LLMs with APIs, databases, and enterprise applications.
• Evidence of measurable operational efficiencies delivered through AI-driven automation.