Staff AI Engineer

The candidate will have responsibilities across the following functions: Inference Optimisation:

• Drive TTFT below 400ms for multi-step agent pipelines.
• Streaming optimisation: first token to user while sub-agents are still running.
• KV cache strategy, prompt compression, and 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-Synthesise pipelines that run sub-agents in parallel DAGs, not sequential chains.
• Build reliable orchestration on top of Temporal: retries, timeouts, partial failure recovery, and idempotency.
• Structured output enforcement: JSON schema validation, retry loops on malformed LLM output, and graceful degradation.
• Tool call design: schema design that LLMs actually follow reliably across providers.

Evaluation and Harness:

• Own the eval 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, malformed tool responses.

Infrastructure:

• Model serving and cold start optimisation.
• Async worker architecture for parallel sub-agent execution.
• Observability: trace every token, every tool call, every synthesis step.