Applied AI Senior EngineerLocation: Austin, TX/Sunnyvale, CA (Onsite) Relocation Works Duration: 12+ MonthsJob DescriptionMust-Have RequirementsBackend/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 demosAgentic SystemsHas 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 modesLLM EvaluationUnderstands how to evaluate LLM outputs and the inherent challenges (non-determinism, quality measurement, regression detection)Hands-On EngineerNot just an architect — writes code, debugs production issues, deploys their own workPreferred / 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 focusScreening Questions for Candidates (MUST SHARE THE ANSWER IN THE SUBMISSION)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