Vice President, AI Ecosystem & Developer Platform

LPL Financial•Charlotte, CA

About The Position

We are building a next-generation AI Ecosystem —an internal “App Store for AI-enabled products” that enables teams to rapidly discover, integrate, and scale trusted AI capabilities across the enterprise. This role will lead the strategy and execution of a developer-first platform that standardizes how AI is built, secured, consumed, and scaled—driving adoption through reusable components, curated catalogs, and seamless integration into engineering workflows. You will operate at the intersection of platform engineering, developer experience, and AI governance , shaping an ecosystem that accelerates innovation while maintaining enterprise trust and compliance.

Requirements

Minimum of 10 years of experience in software engineering, platform engineering, developer platforms, API ecosystems, or enterprise technology platforms.
Minimum of 5+ years of leadership experience managing platform, developer experience (DX), API, or AI engineering teams.
Minimum of 3+ years of experience building and scaling AI/LLM-enabled platforms, products, or developer tooling, including agents, MCP servers, and AI integration patterns.
Minimum of 3+ years of hands-on experience with API management platforms such as Kong, Apigee, MuleSoft, AWS API Gateway, or Azure API Management.
Experience building and driving adoption of enterprise-scale internal platforms, developer marketplaces, service catalogs, app stores, or ecosystem products used across multiple engineering teams.
Proven Developer Champion mindset —deep empathy for internal engineering teams.
Developer Champion: Deep empathy for internal engineering teams with a focus on usability, simplicity, and developer productivity.
Customer-Obsessed: Prioritizes speed, clarity, and a best-in-class developer experience.
Ecosystem Thinker: Designs reusable, composable solutions that scale across teams and business units.
Adoption-Focused: Measures success through platform usage, integration velocity, and developer satisfaction.
Collaborative Leader: Builds alignment across platform, security, product, and engineering organizations.
Bias for Action: Delivers practical, high-impact solutions in a rapidly evolving AI landscape.
Strategic Platform Builder: Proven ability to create and scale enterprise platforms, standards, and reusable capabilities.

Nice To Haves

Experience with: Kong AI Gateway / API gateways, MCP servers and emerging AI integration patterns, and Enterprise-grade platform and tooling development
Experience in building internal platforms, marketplaces, or ecosystem products
Understanding of AI/LLM integration patterns, prompt engineering, and agent frameworks
Experience implementing AI governance, security controls, identity and access management, and enterprise AI standards.
Strong understanding of prompt engineering, RAG, context management, tool calling, and AI evaluation frameworks.
Experience partnering with Engineering, Security, Architecture, and Product leaders to define platform strategy and execution.

Responsibilities

AI Ecosystem Platform & Catalogs: Design and operationalize a centralized AI App Store experience for internal developers. Build and maintain curated catalogs, including: MCP Server Catalog (Model Context Protocol servers), Reusable Agents Catalog, Prompt and Context Libraries, Evaluation Templates and benchmarking frameworks. Ensure discoverability, standardization, and high-quality onboarding across all assets.
Secure AI Access & Integration: Lead development of a secure AI browser experience and SaaS integrations. Define patterns for safe, compliant AI consumption across tools and workflows. Establish enterprise guardrails for identity, data access, and usage governance.
API & Gateway Enablement: Extend and operationalize Kong AI Gateway capabilities. Build and manage custom Kong plugins for: Enable consistent, secure, and scalable AI access patterns.
Developer Experience & Adoption: Deliver a best-in-class developer starter kit, documentation, and quickstart guides. Champion internal developer experience through: Clear integration patterns, Self-service onboarding, High-quality documentation and examples. Drive adoption through usability, reliability, and ecosystem engagement.
Ecosystem Strategy & Collaboration: Define and execute an ecosystem-first strategy across teams and domains. Partner with platform, security, and application teams to ensure alignment. Identify reusable capabilities and scale them across the organization.
Impact: Establish a standardized, secure, and scalable AI consumption model across the enterprise. Accelerate delivery of AI-enabled products through reusable components and patterns. Reduce duplication and risk while increasing velocity and innovation. Position the organization as a leader in enterprise AI platform engineering.