Principal Software Engineer - AI Foundations

Toast Toast · Enterprise · Dublin, Ireland · R & D : Cloud Service Infra : AI Foundations

Principal Software Engineer for Toast's AI Foundations team, focusing on building the infrastructure, governance, and tooling for AI across the product development lifecycle. The role involves setting technical direction for agentic workflows, LLM proxy, AI key management, observability, and plugin marketplace, with a full-stack responsibility.

What you'd actually do

  1. Define and drive the technical architecture for AI Foundations platform, spanning backend services, frontend tooling, and third party integrations
  2. Lead full-stack design and delivery across the LLM proxy, AI key management, observability pipelines, and internal plugin marketplace
  3. Architect and deliver autonomous agents and MCP services that participate in the software development lifecycle
  4. Set and uphold engineering standards across the team: code quality, system design, testing, and operational practices
  5. Mentor and technically lead engineers across all levels through design reviews, code reviews, and pairing

Skills

Required

  • Designing and delivering scalable software systems
  • Full-stack background spanning backend services and frontend or developer-facing interfaces
  • Leading technical architecture for a platform, product, or engineering team
  • Mentoring engineers and raising the technical bar of a team
  • Building, deploying, or operating LLM-powered agents or AI platform infrastructure
  • Familiarity with AI coding assistants and experience extending them
  • Experience with MCP, tool use patterns, or agentic frameworks
  • Modern backend (Java, Kotlin, or similar) and frontend (TypeScript, React, or similar) technologies
  • Building user-facing product interfaces

Nice to have

  • A2A (Agent-to-Agent) protocols and multi-agent orchestration patterns
  • Machine learning concepts, model evaluation, or ML infrastructure
  • Developer experience platforms or internal tooling
  • Observability tooling for AI/LLM systems

What the JD emphasized

  • AI platform infrastructure
  • autonomous agents
  • LLM-powered agents
  • agentic workflows
  • multi-agent orchestration patterns

Other signals

  • AI platform team
  • LLM Proxy
  • autonomous agents
  • software development lifecycle