Applied AI Engineer

About the role

We are seeking a hands-on engineer with deep backend and distributed systems experience who has shipped AI/LLM features to real users at scale. You will build and operate production AI agents, skills, tools, and MCP integrations.

Must-have requirements

• Backend/Systems Experience: 3+ years building production backend or distributed systems (pre-AI experience required)
• Production AI Systems: Has shipped AI/LLM features serving real users at scale — not just prototypes or demos
• Agentic Systems: Has built AI agents, skills, tools, or MCP (Model Context Protocol) integrations
• Python: Proficient for backend development
• Secondary Language: Working knowledge of Go, TypeScript, or Rust
• Cloud Infrastructure: Deep experience with AWS/GCP/Azure — cost optimization, compute decisions, not just deployment
• Container & Orchestration: Hands-on with Docker and Kubernetes — can build, deploy, debug, and scale services themselves
• LLM Integration: Understands token economics, context limits, rate limiting, structured outputs, API failure modes
• LLM Evaluation: Understands how to evaluate LLM outputs and the inherent challenges (non-determinism, quality measurement, regression detection)
• Hands-On Engineer: Not just an architect — writes code, debugs production issues, deploys their own work

Preferred differentiators

• Built multi-step agentic workflows with tool use and function calling
• Experience with agent orchestration frameworks (LangGraph, CrewAI, Claude Agent SDK, Google ADK, OpenAI ADK)
• Built guardrails, fallbacks, or graceful degradation for AI systems
• Streaming inference and async agent orchestration
• Cost/latency optimization: caching, batching, prompt compression
• ML observability tools: Langfuse, Arize, Braintrust, W&B
• Retrieval systems (vector search, hybrid search) — as a tool, not the focus

Screening questions for candidates

• "Describe a production AI agent or skill system you built. What broke and how did you fix it?"
• "Have you built MCP servers/integrations or custom tool-use systems for LLMs?"
• "How do you evaluate whether an LLM-based feature is working well? What makes this hard?"
• "Walk me through how you'd deploy and scale an AI service on Kubernetes."

Not a fit if

• Primarily a model trainer/fine-tuner (we're not training models)
• AI experience is mainly academic, research, or tutorial-based
• No production systems experience (only notebooks/demos)
• Looking for entry-level role with heavy mentorship
• Background is primarily data science/analytics rather than engineering
• "Architects" who don't write or deploy code themselves

Pay

$70,000–$135,000 a year

Location

Cupertino, CA