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Tier-1 employer and senior role but highly specialized GenAI skills reduce candidate pool.
Role requires enterprise banking domain knowledge, governance, and agentic LLM integration, limiting cross-industry transferability.
Explicit 18+ years and 7+ AI experience plus mandatory hands-on LLM and Python requirements.
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Lead end-to-end deployment of LLM-powered agentic AI solutions integrated into banking operations for automation and decision support.
Conduct discovery and process analysis to identify AI opportunities and define KPIs for measurable business impact including cost reduction and productivity gains.
Design, develop, and maintain reusable agentic AI workflows and capabilities using MCP tools and integrate with enterprise systems ensuring scalability, governance, and responsible AI use.
18+ years of overall industry experience with 7+ years in Artificial Intelligence Solutions or equivalent.
Hands-on experience with major LLM platforms (Claude, Gemini, GPT) and practical expertise in LLM model evaluation, prompt engineering, and solution deployment.
Ability to work onsite as per Wells Fargo's operating model with up to 10% travel and flexible shift timings.
Advanced Python proficiency for agentic system engineering and integration skills with enterprise APIs and data sources.
Proven track record of delivering production-grade LLM-driven agentic systems in regulated enterprise environments with attention to reliability and governance.
Strong expertise in LLM architectures including multi-agent coordination, function calling, RAG pipelines, and contextual knowledge integration.
Experience working closely with business stakeholders to translate processes into scalable AI solutions defining success metrics and demonstrating business impact.