VP, AI Platform Engineering

LPL Financial•San Diego, CA

3d•$163,875 - $273,125

About The Position

VP, AI Platform Engineering will be responsible for building and operating LPL Financial's enterprise AI platform that enables teams to develop and run reliable, secure, and governed AI and agent-based systems at scale. Key accomplishments in the role include: Building a high-reliability platform used by multiple engineering teams Establish consistent engineering standards for AI development Eliminate duplication of AI infrastructure across teams Ensure AI systems operate safely within enterprise governance and regulatory constraints Partner closely with architecture, security, and product engineering teams This role leads the engineering organization responsible for building and operating the AI Hub platform—the runtime infrastructure that powers AI-enabled software across the enterprise. The platform provides the control plane for AI systems, enabling teams to safely integrate large language models, build agent-based workflows, access enterprise knowledge, and execute automated actions through secure tools. The focus of this role is production-grade engineering: reliability, safety, scalability, and operational governance of AI systems.

Requirements

Minimum of 12 years software engineering experience
Minimum of 5 years leading platform or infrastructure engineering teams
Experience in distributed systems and cloud platforms
Experience with modern AI systems such as LLMs, RAG architectures, or agent workflows
Experience running large-scale production systems
Experience building internal developer platforms or infrastructure services
Experience building ML or AI platforms
Experience operating systems in regulated or enterprise environments
Experience establishing and growing high performing teams
Strong mentorship skillset
Executive presence and ability to translate technology to business value

Responsibilities

AI Runtime & Agent Orchestration Platform Design and operate the runtime infrastructure that supports AI-driven applications and agents. This includes: Agent execution frameworks for multi-step workflows Orchestration of model calls, tools, and knowledge retrieval State management for long-running AI workflows Human-in-the-loop execution patterns Support for multi-agent coordination and automation
Model Access & AI Service Layer Provide a standardized and governed way for engineering teams to interact with AI models. This include: Model gateway and routing across AI providers Model versioning and lifecycle management Token management and cost controls Consistent APIs for model access across applications
Enterprise Knowledge & Retrieval Infrastructure Build the shared infrastructure that enables AI systems to safely access enterprise knowledge. This include: Document ingestion and indexing pipelines Embedding and retrieval services Vector and graph-based knowledge systems Secure access controls for enterprise data
Tool Execution & Integration Framework Enable AI systems to interact safely with enterprise services and applications. This include: Secure tool registry for AI agents Integration with internal APIs and enterprise systems Authorization and policy enforcement for agent actions Safe execution of automated workflows
Safety, Governance & Control Establish the runtime safeguards required to operate AI systems in regulated environments. This includes: Prompt and input validation Guardrails for agent decision-making Policy enforcement for sensitive operations Compliance controls and auditability
Observability, Reliability & Cost Management Ensure AI systems can be operated reliably at scale. This include: Tracing and monitoring for AI workflows Evaluation and quality monitoring Usage tracking and cost telemetry Incident response and operational tooling