AI Process Architect

KeyBank

Cleveland (OH)

Hybrid

USD 71,000 - 125,000

Full time

6 days ago

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Job summary

KeyBank's Real Estate Capital Real Estate Capital & Technology Optimization team is seeking an AI Process Analyst to support AI and process improvement initiatives across Real Estate Capital (REC) and KeyBank Commercial Servicing (KCIS).

You will partner with bankers, analysts, underwriters, portfolio managers, servicing professionals, and operations staff to map workflows, identify opportunities, and translate them into practical AI-enabled solutions.

Qualifications

Bachelor’s degree or equivalent combination of education and relevant experience.
Practical experience applying generative AI or automation tools to business problems, including developing instructions and evaluating results.
Proficiency with Microsoft Copilot Studio, Power Automate, Google Gemini or comparable enterprise platforms.
Demonstrated experience in business analysis, process improvement, operations transformation, or technology delivery.
Understanding of the differences between AI‑based reasoning and rules‑based processing, and when each is appropriate.
Strong analytical, problem-solving, written communication, and stakeholder management skills.
Ability to independently lead discovery sessions and translate complex workflows into clear requirements and actionable improvements.
Ability to work across business teams, manage shifting priorities, incorporate feedback, and deliver useful results in short iterations.

Responsibilities

Understand business processes and identify opportunities.
Work with REC and KCIS business teams to understand workflows and requirements.
Map current processes including activities, decisions, handoffs, systems, and documents.
Identify opportunities to reduce manual effort and accelerate turnaround times across lending and servicing.
Evaluate AI, automation, system enhancements, or process changes for practical solutions.
Translate opportunities into deliverable AI-enabled solutions.
Develop and refine AI instructions, reusable skills, and low-code prototypes.
Define focused use cases with inputs, outputs, rules, exceptions, and acceptance criteria.
Break larger opportunities into manageable increments for quick testing and delivery.
Define data, document, integration, and workflow requirements with technical teams.
Create clear requirements and documentation for implementation and maintenance.
Test performance and support deployment with representative scenarios.
Assess AI outputs for accuracy, consistency, and evidence.