VP, AI Innovation & Enablement Leader

LPL Financial•San Diego, CA

About The Position

This VP, AI Innovation & Enablement leader will lead the organization responsible for identifying emerging AI technologies, developing reusable patterns, and enabling teams across the enterprise to adopt them effectively. The team serves as the bridge between new AI capabilities and production implementation, ensuring the organization stays current with advances in generative AI and agent-based systems while maintaining strong engineering discipline. This group works closely with the AI platform team, architecture, cybersecurity and AI solutions teams to translate experimentation into reusable patterns, reference architectures, and enterprise adoption.

Requirements

Minimum of 10 years experience in software engineering, machine learning engineering, or applied AI
Experience designing or experimenting with modern AI systems (LLMs, RAG, or agent workflows)
Experience with architecture thinking and ability to translate experimentation into practical engineering patterns
Experience advising engineering teams or leading platform adoption initiatives
Strong communication skills with the ability to engage both engineers and technical leadership
Experience working in enterprise platforms or developer platforms
Experience evaluating and delivering emerging technologies and frameworks
Background in generative AI systems or applied machine learning
Experience establishing and growing high performing teams
Executive presence and ability to translate technology to business value

Responsibilities

AI Capability Discovery Continuously evaluate emerging AI technologies and techniques that can meaningfully improve enterprise AI systems, including: Large language models and reasoning models Agent frameworks and orchestration patterns Advanced retrieval architectures Multimodal AI systems Evaluation and benchmarking techniques
Applied Experimentation Develop prototypes and reference implementations that validate new AI approaches, and produce reusable engineering patterns (not stand alone demos), including: Agent-based workflows Retrieval and knowledge access patterns Prompt engineering and reasoning workflows Tool-using AI systems
Reusable Asset Development Translate experimentation into practical assets that accelerate the development of production AI systems across the organization: Reusable templates for AI applications Agent workflow frameworks RAG and knowledge integration patterns Small models/ synthetic data
Enterprise Enablement Help engineering teams adopt AI capabilities effectively by: Advising teams designing AI-enabled systems Providing guidance on safe and reliable AI system design Running internal technical workshops and onboarding sessions Documenting best practices for AI application development
AI Adoption & Technical Evangelism Drive awareness and adoption of AI capabilities across the enterprise, including: Demonstrating practical AI use cases and reference implementations Showcasing successful AI deployments internally Organizing engineering showcases, workshops, and hackathons Promoting consistent use of the enterprise AI platform Demonstrations are expected to evolve into reusable patterns or production implementations.