Staff Software Engineer, AI Foundations

Toast Toast · Enterprise · Bangalore, India · R & D : Cloud Service Infra : AI Foundations

Staff Software Engineer for Toast's AI Foundations team, focusing on building core AI platform infrastructure like LLM Proxy and observability pipelines, as well as architecting and delivering autonomous agents for the SDLC. The role involves leading technical design, driving adoption of agentic practices, and mentoring engineers.

What you'd actually do

  1. Design, build, and ship core AI platform infrastructure, including the LLM proxy, AI key management, and observability pipelines powering Toast's internal AI ecosystem
  2. Architect and deliver autonomous agents that participate in the SDLC, including the AI Review GitHub App and Developer Platform MCP integrations
  3. Build on and help maintain the internal plugin marketplace, developing plugins that encode Toast's architectural standards, PR patterns, and quality practices directly into AI assistant behavior
  4. Lead technical design and implementation of MCP (Model Context Protocol) services and no-code service templates that accelerate AI-powered development across Toast
  5. Drive adoption of agentic development practices through tooling, internal evangelism, and hands-on enablement across engineering teams

Skills

Required

  • 8+ years of experience designing and implementing scalable backend services
  • Strong foundation in Java, Kotlin, or another object-oriented language, with experience building scalable backend services
  • Demonstrated experience building, deploying, or operating LLM-powered agents or AI-assisted developer tooling
  • Deep familiarity with AI coding assistants (e.g., Claude Code, Cursor, GitHub Copilot) and the ability to extend them through custom plugins, skills, or hooks
  • Hands-on experience with MCP (Model Context Protocol), tool use patterns, or agentic frameworks
  • Strong prompt engineering skills and intuition for how LLM behavior changes with context, instructions, and tool definitions
  • Proven track record of technical leadership: influencing architecture decisions, setting quality standards, and mentoring peers
  • Experience with distributed systems, API design, and cloud-native infrastructure
  • Strong customer empathy and the ability to translate developer pain points into platform solutions

Nice to have

  • Familiarity with A2A (Agent-to-Agent) protocols and multi-agent orchestration patterns
  • Experience with machine learning concepts, model evaluation, or ML infrastructure
  • Experience building developer experience platforms, internal tooling, or API gateways
  • Familiarity with observability tooling for AI/LLM systems (e.g., Langfuse, DataDog)

What the JD emphasized

  • core AI platform infrastructure
  • autonomous agents
  • LLM Proxy
  • AI Key Management
  • Observability pipelines
  • autonomous agents that participate directly in the software development lifecycle
  • AI infrastructure
  • LLM-powered agents
  • AI-assisted developer tooling
  • MCP (Model Context Protocol)
  • tool use patterns
  • agentic frameworks
  • prompt engineering skills

Other signals

  • building AI infrastructure
  • autonomous agents
  • LLM Proxy
  • developer tooling