Lead AI Workflow Designer & Prompt Architect

Wells Fargo•Irving, TX

3d•Hybrid

About The Position

Wells Fargo is seeking a Lead AI Workflow Designer & Prompt Architect to design, develop, and optimize AI-powered workflows using large language models, AI agents, and orchestration frameworks to solve complex business challenges. The role combines strategic problem framing, prompt engineering, workflow architecture, and technical implementation to deliver scalable, reliable AI solutions aligned with business needs. The position bridges business objectives and AI execution by translating ambiguous challenges into structured, multi-step workflows coordinating prompts, tools, context, data, and agent interactions. The role independently assesses system behavior, prototypes solutions, troubleshoots workflow issues, and improves AI performance through experimentation and operational insights. The role follows Wells Fargo's hybrid operating model, with three days on site and two remote, at a listed location. Travel of up to 10% is expected. Remote work locations are not available; candidates outside a listed location must commit to self-relocation within an agreed timeframe.

Requirements

5+ years of Artificial Intelligence Solutions experience, or equivalent demonstrated through work experience, training, military experience, education, or a combination of these.
Ability to work on site according to Wells Fargo's hybrid operating model: three days on site and two remote.
Ability to travel up to 10% of the time.

Nice To Haves

Experience designing, implementing, and optimizing multi-step LLM, generative AI, and agentic workflows.
Strong problem-solving skills and ability to translate complex business challenges into scalable AI solutions.
Expertise in prompt engineering, human-in-the-loop workflows, and AI performance optimization.
Ability to troubleshoot and diagnose issues across prompts, agents, context management, integrations, and workflow execution.
Proficiency in Python, APIs, JSON, Markdown, and working with structured and unstructured data.
Experience using AI-assisted development tools such as GitHub Copilot, Claude Code, Cursor, or similar platforms.
Knowledge of agent orchestration frameworks, multi-agent architectures, and modern workflow orchestration patterns.
Understanding of RAG, context engineering, memory management, tool calling, and agent planning methodologies.
Experience integrating AI solutions with enterprise applications, automation platforms, cloud AI services, and business processes.
Strong communication and collaboration skills, and expertise in governance, risk, responsible AI, evaluation, monitoring, compliance, and operational controls.

Responsibilities

Partner with business, product, technology, and operations teams to understand challenges and identify opportunities for AI and agentic solutions.
Translate business problems into structured AI workflow designs with logical stages and execution paths.
Design, develop, and optimize multi-step prompt workflows for AI agents performing reasoning, decision support, content generation, analysis, and task execution.
Define workflow architecture, including prompts, context requirements, tool interactions, orchestration logic, handoffs, dependencies, and output requirements.
Determine how information, memory, context, and intermediate outputs are managed during workflow execution.
Design and maintain prompt libraries, reusable workflow templates, and orchestration patterns for multiple business use cases.
Collaborate with product and engineering teams to integrate AI workflows into enterprise applications and business processes.
Analyze workflow logs, model outputs, and intermediate agent responses to identify failures, performance degradation, and inconsistent results.
Troubleshoot prompt design, context management, agent behavior, tool integrations, data pipelines, file structures, APIs, and execution environments.
Evaluate workflow effectiveness using qualitative and quantitative measures and implement improvements to reliability, accuracy, efficiency, and user outcomes.
Improve context engineering strategies for information retrieval, grounding, memory management, and response quality.
Develop lightweight automation, scripts, and utilities for workflow development, experimentation, testing, evaluation, monitoring, and operational efficiency.
Perform data inspection, transformation, validation, and analysis to support AI workflow development and troubleshooting.
Rapidly prototype solutions when existing tools, workflows, or capabilities do not fully address a business need.
Contribute to AI governance, risk management, quality assurance, and responsible AI practices, ensuring workflows follow established standards and controls.
Stay informed about emerging AI technologies, agent frameworks, orchestration platforms, prompt engineering techniques, and industry best practices.
Follow applicable risk and compliance programs, policies, procedures, escalation and remediation practices, and make sound risk decisions.