This role pays less than
69%
of similar roles. Most pay
$211,200–$288,150
— the shaded band above.
At the midpoint, this role pays about
$221k
versus about
$250k
for comparable roles.
Based on 239 similar postings.
Employer
About Capital One Financial
Capital One Financial is a bank holding company specializing in credit cards, auto loans, banking, and savings products, known for its data-driven approach to consumer and commercial finance. Industry: Financial Services & Banking
Capital One Financial currently has
998 open roles
on FindRole.
Listed pay typically runs
$197,300–$225,100
across 992 roles with salary data.
As part of the Enterprise Learning & Development team, the Director, AI Learning Enablement will lead the strategy for an enterprise-wide AI transformation. This role involves designing and implementing learning initiatives to drive workforce readiness, accelerate AI fluency, and promote operational tool adoption across the organization. The successful candidate will create a comprehensive learning roadmap, manage internal and external AI toolkits, and translate complex technical concepts like LLM workflows, prompt engineering, and agentification into accessible enablement tools. Key responsibilities include managing cross-functional projects, driving large-scale change management, and utilizing data analytics to measure training ROI. The role requires proficiency with generative AI platforms such as Claude and Gemini, along with a deep understanding of data privacy principles. This position addresses the critical business challenge of transitioning the workforce into an AI-enabled operating model through structured, scalable learning programs.
AI
ML
Azure OpenAI
Microsoft 365 Copilot
Vector Databases
MLOps
CI/CD
Security Automation
Governance as Code
Software Engineering
Data Classification
Vulnerability Management
Generative AI
Conversational AI
Machine Learning
Robotic Process Automation (RPA)
Intelligent Automation
Workflow Orchestration
Workday
ServiceNow
Microsoft Copilot
Agile
Product Management
Business Process Design
Software Development Management
Quality Assurance (QA)
AI
ML
RAG
LLMs
Generative AI
Distributed Systems
Platform Engineering
Context Orchestration
Memory Systems
Retrieval Systems
Real-time Inference
Observability
System Optimization
Agent-based Systems
AI
Machine Learning
Reinforcement Learning
Model Training
AI-for-systems
hardware–software stack
Compiler
Inference
Research Prototyping
Training Pipelines
Performance Optimization