Senior AI Engineer

The core responsibilities for the job include the following:

Inference Optimization:

• Drive TTFT below 400ms for multi-step agent pipelines.
• Streaming optimization: the first token to the 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 and Harness:

• Own the evaluation framework end-to-end: ground truth datasets, automated scoring pipelines, and 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, and 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.

Requirements:

• You've built something that runs in production at a meaningful scale and you understand why it's fast (or why it isn't).
• 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.
• Stack familiarity (we care more about depth than match): Go, Python, Temporal, Kafka, PostgreSQL, and Docker.

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.
• 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.

Weaker signal (but not disqualifying):

• 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.