VP, AI Platform Engineering

Lead with Purpose, Unlock Your Team’s Passion

At LPL, people leaders hold the key to the employee experience — shaping culture, driving performance, and guiding individuals to new heights. Because when that happens, we all win – clients, LPL, and most importantly our, employees.

If you're ready to lead with intention and discover what’s possible, LPL Financial invites you to apply today.

Why This Role Matters

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

Job Overview

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.

Key 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

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What are we looking for?

We’re looking for strong collaborators who deliver exceptional client experiences and thrive in fast-paced, team-oriented environments. Our ideal candidates pursue greatness, act with integrity, and are driven to help our clients succeed. We value those who embrace creativity, continuous improvement, and contribute to a culture where we win together and create and share joy in our work.

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

Core Competencies:

• 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

Pay Range:

$163,875-$273,125/year

Actual base salary varies based on factors, including but not limited to, relevant skill, prior experience, education, base salary of internal peers, demonstrated performance, and geographic location. Additionally, LPL Total Rewards pa