Applied AI Senior Engineer

Job DescriptionMust-Have RequirementsRequirement DetailsBackend/Systems Experience3+ years building production backend or distributed systems (pre-AI experience required)Production AI SystemsHas 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) integrationsPythonProficient for backend developmentSecondary LanguageWorking knowledge of Go, TypeScript, or RustCloud InfrastructureDeep experience with AWS/GCP/Azure — cost optimization, compute decisions, not just deploymentContainer & OrchestrationHands-on with Docker and Kubernetes — can build, deploy, debug, and scale services themselvesLLM IntegrationUnderstands 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 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 themselvesLocation : Sunnyvale, CA/Austin, TXSalary Range: $70,000-$150,000 a year