Applied AI Engineer

Title: Applied AI Engineer — Inference & Agent Systems

**Location:**United States

What We're Building

Arcana is building AI agents that synthesize information across heterogeneous sources and deliver structured, reasoned answers in real time. The product only works if the agents are fast, reliable, and correct, not approximately correct.

Our stack: Go + Temporal for orchestration, a Plan-Execute-Synthesize agent architecture, and an evaluation harness we use to measure every regression. The problems are hard. The latency bar is aggressive. The accuracy requirements are unforgiving.

The Work

Inference Optimization

- Drive TTFT below 400ms for multi-step agent pipelines

- Streaming optimization: first token to user while sub-agents are still running

- KV cache strategy, prompt compression, dynamic context window management

- Multi-provider routing: model selection by latency, cost, and task type across OpenAI, Anthropic, Gemini, and open-weight models

Agent Architecture

- Design and implement Plan-Execute-Synthesize pipelines that run sub-agents in parallel DAGs, not sequential chains

- Build reliable orchestration on top of Temporal: retries, timeouts, partial failure recovery, idempotency

- Structured output enforcement: JSON schema validation, retry loops on malformed LLM output, graceful degradation

- Tool call design: schema design that LLMs actually follow reliably across providers

Evaluation & Harness

- Own the eval framework end to end: ground truth datasets, automated scoring pipelines, regression detection on every PR

- LLM-as-judge pipelines for qualitative output assessment

- Latency regression testing - p50/p95/p99 tracked across every deployment

- Adversarial test case design: ambiguous queries, missing data, conflicting sources, malformed tool responses

Infrastructure

- Model serving and cold start optimization

- Async worker architecture for parallel sub-agent execution

- Observability: trace every token, every tool call, every synthesis step

What We're Looking For

You've built something that runs in production at a meaningful scale and you understand why it's fast (or why it isn't).

Strong signal:

- You've worked on inference pipelines where TTFT was the primary metric and you moved it meaningfully

- You've built multi-step agent systems and you know where they break not from reading papers but from watching them fail in production