Applied AI Engineer

Must-Have Requirements

• 3+ years building production backend or distributed systems (pre-AI experience required)
• Has shipped AI/LLM features serving real users at scale — not just prototypes or demos
• Has built AI agents, skills, tools, or MCP (Model Context Protocol) integrations
• Python – proficient for backend development
• Secondary language – working knowledge of Go, Type Script, or Rust
• Cloud infrastructure – deep experience with AWS/GCP/Azure, focusing on cost optimization and 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 – writes code, debugs production issues, deploys their own work (not just an architect)

• Built multi‑step agentic workflows with tool use and function calling
• Experience with agent orchestration frameworks (Lang Graph, 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

Location:

Austin, TX

Salary Range: $70,000-$130,000 Per a Year