Note: The job is a remote job and is open to candidates in USA. Arcana is building AI agents that synthesize information across heterogeneous sources and deliver structured, reasoned answers in real time. The Senior AI Engineer will focus on optimizing inference pipelines, designing agent architectures, and owning the evaluation framework to ensure high performance and reliability of AI systems.
Responsibilities
- 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
- 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
- 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
- Model serving and cold start optimization
- Async worker architecture for parallel sub-agent execution
- Observability: trace every token, every tool call, every synthesis step
Skills
- You've built something that runs in production at a meaningful scale and you understand why it's fast (or why it isn't)
- 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
- You've written eval harnesses from scratch and you have opinions about what makes a ground truth dataset actually useful
- You've debugged LLM non-determinism in production and built systems resilient to it
- You've worked with streaming LLM responses and built infrastructure around partial output handling
- Stack familiarity: Go, Python, Temporal, Kafka, PostgreSQL, Docker
- You've fine-tuned models but haven't shipped inference systems
- You've used LangChain/LlamaIndex but haven't built the layer underneath
- Strong ML research background without systems exposure
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