Applied AI Engineer

Job description

• Must-Have Requirements Requirement Details 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 1. "Describe a production AI agent or skill system you built. What broke and how did you fix it?" 2.

"Have you built MCP servers/integrations or custom tool-use systems for LLMs?" 3. "How do you evaluate whether an LLM-based feature is working well? What makes this hard?" 4. "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 Location : Cupertino, CA Salary Range: $70,000-$130,000 a year </div