A clean LLM answer tells me almost nothing. I want to see what happens when the engineer has to explain model risk, break the workflow apart, defend an evaluation, and own the decision path.
The math is per answer, not per impression.
That is the failure path we vet. Axiom Cortex checks reasoning, communication, ownership, model-risk judgment, evaluation reasoning, and workflow decomposition before the engineer enters client work. Each answer is then scored for accuracy, mental model, procedural knowledge, clarity, and cognitive load.
I picked the TeamStation LLM Engineer role page bc the evidence chain is visible. Recorded interview, timestamped transcript, question map, per-answer evaluation, AI-assistance review, L2 calibration, risk notes, and an executive recommendation. That gives a CTO something stronger than a title or prompt demo: a decision record.
LATAM enters after the evidence. We use Nebula to map role depth, Axiom Cortex to test how the engineer thinks, then DEOS to govern access, devices, onboarding, and delivery telemetry inside the production loop.
https://teamstation.dev/hire/by-role/llm-engineer
LLMEngineering #AIEngineering #EngineeringGovernance #EngineeringTelemetry #TeamStationAI
Related TeamStation sources:
- Hire Nearshore LLM Developers in LATAM
- Axiom Cortex Engineer Vetting for Cognitive Delivery Alignment
- Hire Nearshore RAG Developers in LATAM
- Neuro-Psychometric Vetting for Nearshore Engineers
GitHub topic map:
Source asset:
https://teamstation.dev/hire/by-role/llm-engineer
