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Available now · Onboard in 7 days
Hire an AI QA Tester who keeps LLMs, RAG, and agents safe and on-spec.
Onboard a senior AI QA Tester to design evaluations, automate prompt regression, run red-team exercises, and gate releases on measurable AI quality and safety thresholds.
QASKRN+9
4.9/5
Trusted by 120+ teams shipping AI products
- 5+ years testing AI / ML systems
- LLM, RAG & agent evaluation
- Red-team & safety expertise
- Onboard in 7 days
Why hire an AI QA Tester
Outcomes you can ship in the first 90 days
Trustworthy AI behavior
Catch hallucinations, drift, and unsafe outputs before users ever see them.
Fewer regressions
Automated prompt and model regression suites stop quality from sliding between releases.
Safer rollouts
Pre-launch red-team exercises and safety gates protect users, brand, and compliance posture.
Measurable AI quality
Quantified scores across accuracy, safety, and UX make AI quality a business KPI.
Core responsibilities
What your AI QA Tester will own
Strategy, regression, evaluations, safety, RAG/agent testing, and release gates — full coverage of the AI quality surface.
Test Strategy for AI
- Define test plans across LLMs, RAG pipelines, and agentic workflows
- Map quality dimensions: accuracy, safety, latency, cost, UX
- Build risk-based test prioritization for AI features
Prompt & Output Regression
- Build golden sets and canonical prompt suites
- Run automated regression on prompt, model, and policy changes
- Detect output drift across providers and model versions
Evaluation Framework
- Set up eval harnesses with rule-based, embedding, and LLM-as-judge methods
- Calibrate evaluators against human reviewers
- Track metrics over time per feature, prompt, and model
Safety, Bias & Red-Teaming
- Run adversarial prompts, jailbreaks, and policy violations
- Audit bias, fairness, and harmful content risks
- Coordinate structured red-team exercises pre-launch
RAG & Agent Testing
- Validate retrieval quality, citations, and grounding
- Test multi-step agents on planning, tool use, and recovery
- Cover edge cases for memory, state, and timeouts
Release Gates & Reporting
- Block releases that fail quality, safety, or cost thresholds
- Produce dashboards for product, eng, and leadership
- Drive postmortems and continuous QA improvement
Skills & expertise
Test engineering meets AI judgment
Testers who can automate at scale, calibrate human evaluations, and apply Responsible AI thinking to risk-rank what to test first.
- AI / LLM testing
- Prompt regression
- Eval framework design
- Red-teaming
- Bias & fairness testing
- RAG evaluation
- Agentic test design
- Test automation
- Performance & load testing
- Python & SQL
- Statistics for QA
- Risk-based testing
AI tools & stack we operate in
Hands-on across AI eval tools, observability, traditional test frameworks, and reporting.
AI Eval Tools
- Promptfoo
- DeepEval
- Ragas
- TruLens
- OpenAI Evals
- LangSmith
Observability
- Langfuse
- Helicone
- Arize
- PromptLayer
- Datadog
- Sentry
Testing Frameworks
- Pytest
- Playwright
- Cypress
- k6
- Locust
- Selenium
LLM Providers
- OpenAI
- Anthropic Claude
- Google Gemini
- Azure OpenAI
- AWS Bedrock
- Open-source models
Data & Storage
- Postgres
- Snowflake
- BigQuery
- S3
- DVC
- Hugging Face
Reporting
- Looker
- Metabase
- Notion
- Jira
- Linear
- Slack
Engagement models
Hire on terms that match your stage
Dedicated Full-time
Senior AI QA Tester embedded full-time to own AI quality end-to-end.
- 40 hrs / week
- Full ownership
- Long-term roadmap
Part-time / Fractional
20 hrs/week of strategic AI QA coverage — perfect for ongoing eval and audit work.
- Eval focus
- Release reviews
- Flexible cadence
Project / Outcome-based
Fixed-scope engagement to deliver an eval suite, red-team report, or QA framework.
- Defined deliverables
- Milestone billing
- Predictable timeline
How to hire
From first call to first sprint in under a week
02
Shortlist in 48 hours
Receive 2–3 pre-vetted AI QA Testers with relevant case studies.
03
Interview & evaluate
Run interviews, case discussions, and an optional paid trial on a real evaluation problem.
04
Onboard in 7 days
Sign, kick off, and start delivery — backed by a 14-day risk-free trial.
FAQs
Hiring an AI QA Tester — questions we hear most
What does an AI QA Tester actually do?+
An AI QA Tester validates AI behavior — accuracy, safety, latency, and UX — using evaluations, prompt regression suites, red-team exercises, and structured human review. They make AI quality measurable and gate releases against it.
How is AI testing different from traditional QA?+
AI outputs are probabilistic and prompt-sensitive. Testers must combine deterministic test cases with embedding similarity, LLM-as-judge evaluations, structured human review, and safety/bias testing — alongside conventional functional and performance testing.
Can you test RAG and agent systems?+
Yes. We validate retrieval quality, grounding, citations, multi-step agent planning, tool calls, recovery from failures, and stateful memory — using both automated and human-in-the-loop methods.
Do you do red-teaming and safety audits?+
Absolutely. We run adversarial prompts, jailbreaks, policy probes, and harmful-content tests, and produce reports and remediation plans aligned with Responsible AI standards.
How do you integrate with our release process?+
We define release gates with clear quality, safety, and cost thresholds, and integrate eval suites into your CI/CD so every prompt, model, or policy change is automatically validated.
How quickly can the tester start?+
Most engagements start within 7 days of contract signing. Urgent needs can be accelerated to 72 hours.
Ready to hire your AI QA Tester?
Share your AI quality goals and we'll send a shortlist of pre-vetted AI QA testers within 48 hours — backed by a 14-day risk-free trial.
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