Risk, Fraud & Compliance

Skill areas

AI Quality / Testing Analyst

An AI Quality / Testing Analyst designs and executes the evaluation processes that ensure AI systems — particularly those using large language models or machine learning models — behave reliably, safely, and as intended before and after deployment. Day-to-day work involves writing test cases that probe AI system behaviour across a wide range of inputs including edge cases and adversarial examples, running structured evaluations to measure output quality against defined criteria, documenting failure modes and unexpected behaviours, collaborating with prompt engineers and ML engineers to investigate root causes, and maintaining evaluation datasets and benchmarks. The role requires a combination of systematic QA discipline, curiosity about AI system behaviour, and enough technical literacy to understand what is being tested and why it might fail.

Career information

Everything you need to know about this role

Salary, eligibility, background, qualifications, and where people typically work.

Expected salary range

P25 and P75 are the 25th and 75th percentiles for UK full-time pay (national benchmarks). UK median is the national full-time median salary.

P25UK medianP75

£29,000£39,000£54,000

Source: ONS Annual Survey of Hours and Earnings (ASHE), UK full-time gross annual earnings, April 2025 (released Oct 2025).

Entry

0–2 yrs

£32,000–£52,000

Mid

2–5 yrs

£52,000–£75,000

Senior

5+ yrs

£75,000–£110,000

Dot = average of the range; coloured bar shows min–max vs UK benchmarks above.

Eligibility limits

Right-to-work, checks, and role-specific requirements — tap to view.

Right to work in the UK required. No formal licence required. Roles at AI labs or government AI bodies may require SC clearance or enhanced vetting given access to sensitive model capabilities. Financial services AI testing roles may involve FCA-related screening. Some roles involve exposure to potentially harmful AI outputs during red-teaming — candidates should be aware of the nature of this work.

Understand eligibility rules →

How the role developed

AI testing as a distinct discipline emerged alongside the mainstream adoption of large language models from 2022 onwards. Earlier ML systems — classification models, recommender systems — could be evaluated using relatively straightforward accuracy and precision metrics.

LLMs produce open-ended text outputs that cannot be evaluated with the same binary pass/fail approach, and their failure modes — hallucination, bias, unsafe content generation, prompt injection vulnerability — are qualitatively different from conventional software bugs. The field borrowed from software testing, adversarial machine learning research, and human-computer interaction to develop new evaluation methodologies.

Current direction

In the UK, the AI Safety Institute (established 2023) has made AI evaluation a national priority, publishing evaluation frameworks and conducting assessments of frontier AI models. The EU AI Act, which applies to UK firms deploying AI in EU markets, mandates evaluation and documentation requirements for high-risk AI applications.

Sector regulators including the FCA and the Medicines and Healthcare products Regulatory Agency (MHRA) are developing AI-specific guidance that places obligations on organisations to test and validate AI systems used in regulated decisions. These regulatory drivers are creating growing demand for professionals who can design and execute rigorous AI evaluations — not just run a few example prompts and declare the system safe.

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technicalai-quality-testing-analyst

Why they ask

Red-teaming is the practice of adversarially probing AI systems for safety and security failures. It is increasingly expected as part of responsible AI deployment and is a core AI quality skill.

What they want

A clear definition of AI red-teaming, the main attack categories it covers, and a practical methodology for conducting a red-team exercise on a deployed LLM application.

How to structure your answer

Define AI red-teaming — adversarial probing for safety and security failures
Distinguish from conventional red-teaming — AI-specific attack surfaces
Describe the main attack categories — jailbreaking, prompt injection, data extraction, harmful content
Walk through a red-team methodology — scoping, attack design, systematic execution, documentation

Sample answer

AI red-teaming involves systematically attempting to make an AI system behave in ways that are unsafe, harmful, or contrary to its design — finding vulnerabilities before malicious users do. Unlike conventional security red-teaming, which focuses on infrastructure and code vulnerabilities, AI red-teaming targets the model's behaviour: its responses to adversarial inputs, its susceptibility to manipulation through clever prompt design, and its failure to maintain safety constraints under pressure. The main attack categories I would cover are: jailbreaking — attempts to make the model produce content it is designed to refuse, using techniques like role-play framing, hypothetical scenarios, or multi-step manipulation; prompt injection — crafting user inputs that override system instructions, particularly relevant in applications where user content is included in the prompt; data extraction — attempts to make the model reveal information from its system prompt or training data it should not disclose; and harmful content elicitation — testing the model's responses to inputs that could cause harm if answered carelessly, such as questions about dangerous substances or self-harm. My methodology would start with scoping — agreeing which attack categories are in scope and what constitutes a finding — then systematic execution, working through each category with a structured set of attack prompts including known techniques and novel variations. Every finding is documented with the exact input, the output, why it is a problem, and a severity rating. The output is a red-team report that the engineering team can use to improve the system's defences.

Common mistakes

Describing red-teaming as just "trying to hack the AI" without the structured methodology
Not distinguishing AI-specific attack surfaces from conventional security testing
Missing the documentation requirement — findings that are not documented cannot be acted on

Bonus tips

Reference the UK AI Safety Institute's published red-teaming methodologies as a credible framework
Mention that red-teaming is most valuable when combined with automated testing — human creativity finds novel attacks, automation provides coverage at scale
Note that red-teaming should be repeated after significant model or prompt changes — it is not a one-time exercise

AI Quality / Testing Analyst

Everything you need to know about this role

Expected salary range

Eligibility limits

How the role developed

Current direction

Everything you need to know about this role

Expected salary range

Eligibility limits

How the role developed

Current direction

Where this role can take you

Overview

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