careers in test

AI Evaluation & Quality Engineer

“Have you ever wondered what it would be like to test systems where there is no single right answer, and success is measured in probabilities rather than strict assertions? That’s the role of an AI Evaluation & Quality Engineer.”

An AI Evaluation & Quality Engineer is responsible for the systematic testing, scoring, and validation of Generative AI systems, Large Language Models (LLMs), and Retrieval-Augmented Generation (RAG) pipelines. Unlike traditional software testing, they focus on non-deterministic outputs, evaluating models for accuracy, relevance, context compliance, bias, and performance drift over time.

Knowledge Required
  • Probabilistic testing methodologies and semantic evaluation metrics.
  • LLM orchestration architectures (RAG pipelines, vector databases, and embeddings).
  • Synthetic data generation and ground-truth dataset curation.
  • Statistical data analysis and model drift monitoring.
  • AI lifecycle management and MLOps principles.
Skills Required
  • Python programming for data manipulation and test orchestration.
  • Expertise in configuring code-driven AI evaluation frameworks.
  • Advanced prompt engineering for structured evaluation (LLM-as-a-Judge).
  • Ability to isolate software bug regressions from stochastic model behaviors.
  • Strong collaborative skills with data scientists, AI engineers, and product teams.
Typical Responsibilities
  • Creating and maintaining “golden datasets” to bench-test system performance.
  • Implementing automated pipelines to score model outputs for hallucinations, factual accuracy, and context retrieval.
  • Monitoring production models to flag data drift, semantic degradation, or performance drops.
  • Establishing baseline threshold metrics (e.g., BERTScore, G-Eval) to gate deployments.
  • Collaborating with data teams to identify flaws in training, fine-tuning, or embedding processes.
Common Tools

DeepEval, Ragas, Promptfoo, LangSmith, Weights & Biases, Arize Phoenix, Langfuse, Jupyter Notebooks

Connect & Facilitate

This role often overlaps with Data Scientists, ML Engineers, and Software Developers in Test (SDETs), bridging the gap between data-driven machine learning models and rigorous product QA workflows.

Rate Table (National Average)

Note: Due to the high software engineering demands and scarcity of data-literate QA professionals, these roles command a premium over standard engineering rates.

RemunerationValue
Daily Rate (contract)$950 – $1,250
FTE Salary (Permanent)$150,000 – $175,000

Project Hiring Cost (average)

These percentages are derived from an annualized amount. Given the costs involved in sourcing, vetting, and correspondence for a role of this type, a recruiter would expect a minimum fixed fee of 15K, although most recruiters operate on percentages nowadays.

Project Hiring CostValue
Internal HR15-20%
Recruiters25%

Interview Questions

Here are some interview questions you will most likely encounter for this role. While we don’t provide answers, we do clarify the intent behind the questions, which makes them a great resource when researching the role in readiness for an interview.

To evaluate the candidate’s understanding of probabilistic systems and semantic similarity scoring.

To assess structural troubleshooting skills across the retrieval, context window, and model generation phases.

To gauge data literacy and validation hygiene when scaling automated test coverages.

To test the candidate’s practical experience with advanced automated evaluation architectures and their understanding of systemic model limitations like self-preference or position bias.

To assess the candidate’s understanding of long-term software quality maintenance, moving past static test suites to continuous semantic monitoring in a live environment.

ATS Keyphrases

These keywords are commonly used by recruiter Application Tracking Systems to determine the relevance of a CV or cover letter to a specific position description. By ensuring at least a few of these key phrases appear throughout your CV and cover letter, you increase your relevance where an ATS is being used.

AI Model Evaluation, LLM Testing, RAG Pipeline Validation, Ground Truth Curation, Golden Dataset Development, Semantic Similarity Scoring, Hallucination Detection, DeepEval, Ragas, Promptfoo, Model Drift Monitoring, Non-Deterministic Testing, LLM-as-a-Judge, Vector Database Auditing, Bias and Fairness Testing, Synthetic Data Generation, MLOps QA, Context Relevance Metrics, Token Metrics Analysis, Stochastic System Testing