AI evaluator, AI trainer and FDE: these roles contribute at different stages. Understanding their responsibilities helps explain where professional expertise fits into an AI project.
An evaluator tests AI responses and behaviour against defined professional scenarios. They identify errors, check stated limitations and document differences between observed and expected results.
The deliverable: test cases, evidence and actionable recommendations. At Huxpert, professional experience helps identify the situations that actually matter in day-to-day work.
This term can describe preparing examples, annotating data or comparing responses to provide human feedback. Technical teams may then use that data to improve a model.
Huxpert is preparing this domain contribution through reasoned corrections and reference answers. Technical model training is not a service we currently offer.
An FDE works alongside customer teams to design, integrate and deploy solutions. The role combines software engineering with an understanding of practical workflows.
Huxpert can support these teams with scenarios and domain validation. Our role remains human evaluation; we do not present this activity as an FDE service.
A response can sound fluent and convincing while overlooking an essential constraint: missing information in a property inspection, an unverified reference in automotive diagnostics or an impractical restaurant service plan.
A professional examines context, practical consequences and possible corrections. They distinguish an AI error from an application defect, insufficient data or a scenario outside the agreed scope. This distinction helps the product team make the right improvement.
Evaluation complements user feedback by making the tested situations, expectations and observed outcomes explicit. NIST recommends documenting tests and measuring performance in conditions close to the intended use. Read the NIST framework, Measure function.
The sample report illustrates the deliverable. Light and Full assignments differ in coverage and observation conditions. The scope and required skills are confirmed before a quote is issued.
Human involvement means more than approving an answer. It requires being able to understand a suggestion, identify a limitation, make a correction and take back control of a decision. This matters in applications, software and, where suitable testing resources are available, robot-embedded AI.
In a training project, human judgements are one resource among others. Research into reinforcement learning from human feedback, or RLHF, illustrates one technical use of that feedback. Anthropic research on human feedback.
Assignments may fit alongside existing work, depending on demand and availability. Applying does not guarantee selection or a volume of work. Describe your experience, tools and professional boundaries so that each assignment matches your skills.
References reviewed on 25 September 2026. Job titles vary between organisations.