Backend/Systems Experience
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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 xsgimln
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