Applied AI Engineer

Role - Applied AI Engineer

Location - Austin, TX

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

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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