AI Application Developer

Key Responsibilities

• Build AI-powered applications that automate workflows, assist teams, and enhance customer experiences across our restaurant technology platform.
• Design and develop voice AI solutions including conversational agents, phone-based ordering, and real-time customer support.
• Architect Agentic systems—single-agent and multi-agent—that can reason, plan, and execute complex tasks with appropriate human oversight.
• Connect AI systems to company data and services so they can answer questions and take actions based on real information.
• Translate business needs into technical solutions, working closely with non-technical stakeholders to understand problems before jumping to code.
• Monitor and improve deployed AI systems by tracking performance, evaluating outputs, and optimizing for reliability, latency, and cost.

Requirements & Tech Stack

• Strong Python skills with FastAPI and Pydantic.
• Experience with LLM APIs and Agentic frameworks.
• Experience building applications using frontier models and understanding prompt engineering, function calling, and structured outputs.
• Voice AI experience, including working with STT/TTS services (e.g., Deepgram, ElevenLabs) and voice agent frameworks (e.g., LiveKit, Pipecat, Vapi).
• Understanding of real-time audio pipelines, turn-taking, and latency optimization.
• Familiarity with agent interoperability protocols such as A2A and MCP.
• Understanding of how agents connect to tools, data sources, and each other.
• Problem-solving mindset to break down complex problems and find practical solutions.
• Clear communication skills to explain technical concepts, gather requirements, and document system behavior.
• Production experience deploying and maintaining cloud-native applications with proper observability and reliability practices.

Bonus

• Experience deploying voice or conversational AI agents in production.
• Broader voice stack knowledge spanning VAD, speech recognition, synthesis, orchestration, and telephony integration via Twilio, Telnyx, or similar.
• Experience building multi-agent systems or production MCP servers.
• Hands-on work with agent SDKs such as OpenAI Agents SDK, Google ADK, LangGraph, or similar.
• Familiarity with vector databases like pgvector, Pinecone, or Weaviate and RAG patterns.
• Background in model evaluation or building quality pipelines for AI outputs.
• Experience with real-time communication infrastructure such as WebRTC.
• Contributions to open-source AI tooling or published technical writing.