We are looking for an LLM / Agentic Evaluation Rig Engineer to build the system that decides whether our AI output is good enough to ship. Because our commentary sits next to externally reported financials, we cannot rely on vibes — grounding, faithfulness, and hallucination have to be measured, tracked, and gated before anything reaches a customer.
You own the evaluation infrastructure: the datasets, the scorers, the harnesses, and the CI gates that hold the AI and agentic layers to a hard quality bar. You are the team's source of truth on whether a model, prompt, or agent change is actually an improvement — and the one who blocks it if it isn't.
What makes this role different
- You define "good enough to ship" — your gates block regressions in grounding and faithfulness from reaching production.
- Evidence over vibes — every claim is checked against the verified source data it must be grounded in.
- Agentic evaluation — you evaluate multi-step reasoning flows, not just single prompts.
- Real leverage — your rig is how the whole AI team moves fast without breaking trust.
Responsibilities
Datasets & Scorers (35%)
- Build and curate evaluation datasets, including adversarial and edge-case sets with ground-truth labels
- Build scorers for grounding, faithfulness, hallucination, factual consistency, and structured-output validity
- Combine rule-based checks, reference-based metrics, and LLM-as-judge where appropriate
- Verify generated claims map to verified source data — no unsupported statements
Harnesses & CI Gates (30%)
- Build harnesses that run evaluations reproducibly across model, prompt, and agent versions
- Wire evaluation into CI so grounding / faithfulness regressions block releases
- Track quality over time with dashboards and explicit pass / fail thresholds
Agentic Evaluation (25%)
- Evaluate multi-step / agentic flows — routing, tool-use, verification, confirmation
- Build trace capture and step-level scoring for agent runs
- Detect where a flow silen