Vice President, AI Ecosystem & Developer Platform

Job Overview

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.

Responsibilities

• 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:
• 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

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

Core Competencies

• Proven Developer Champion mindset—deep empathy for internal engineering teams
• 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

Preferences

• 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