VP of Software Engineering - Platform
New
Based in United StatesFull-TimeVp
Salary not disclosed
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Job Details
- Experience
- 15+ years of software engineering experience; 5+ years leading engineering organizations at VP/senior director level
- Required Skills
- AWSNode.jsPostgreSQLPythonKubernetesRuby on Rails
Requirements
- 15+ years of software engineering experience with significant leadership in enterprise SaaS, platform engineering, infrastructure, integrations, or AI-driven systems.
- 5+ years of experience leading engineering organizations at VP, senior director, or equivalent executive level.
- Strong ability to translate business strategy into technical platform architecture, operating models, and execution plans.
- Deep experience building and scaling platform services such as developer tooling, APIs, data platforms, and integration frameworks.
- Hands-on technical knowledge of cloud-native systems including AWS, Kubernetes, and modern backend technologies such as Ruby on Rails, Node, Python, and PostgreSQL.
- Experience with AI platforms and tools such as OpenAI SDK, MCP, and agent-based architectures in production environments.
- Strong architectural judgment with the ability to evaluate trade-offs in scalability, reliability, security, and operational complexity.
- Proven ability to lead large, multi-team organizations and develop senior engineering leaders and architects.
- Strong communication skills with the ability to influence both technical and executive audiences.
- Strategic mindset focused on platform reuse, developer experience, and measurable engineering productivity.
Responsibilities
- Define and lead the end-to-end platform engineering strategy, roadmap, operating model, and execution across application platforms, AI systems, data infrastructure, and integrations.
- Build and scale enterprise-grade shared services including APIs, authentication, developer portals, logging, audit systems, and platform libraries.
- Own the agentic AI platform strategy, including architecture for tool execution, model interaction, observability, evaluation, permissions, and production deployment.
- Lead the transformation toward an AI-native software development lifecycle.
- Define and govern enterprise integration strategy across APIs, MCP, and connectors.
- Lead data platform and analytics strategy, including data pipelines, warehousing, and governance.
- Manage and scale multiple engineering organizations across platform engineering, data, AI, and developer experience.
- Partner with executive leadership to align platform capabilities with business priorities.
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