Staff Augmentation
AI/LLM Testing EngineersVetted. Embedded. Ready in Days.
Engineers from our AI Quality Engineering practice, trained on LLM evaluation, prompt regression, RAG pipeline Testing, agent validation, and hallucination detection. A skill set few QA teams have today, embedded in yours.
Experience levels
Mid · Senior · Lead
Engagement model
Full-time dedicated or scoped engagement, billed monthly
Typical start
Typically within days of a scoping call
Recruitment fee
Zero, monthly billing only
Core stack & skills
A capability most QA teams do not have yet
Testing an LLM feature is not conventional QA with a different subject.
Testing an LLM feature is not conventional QA with a different subject. Outputs are non-deterministic, so exact-match assertions are useless. Failures are silent, a fluent, confident, wrong answer raises no exception and changes no status code. Quality is a distribution rather than a boolean, which means the tooling, the vocabulary and the definition of a passing build all have to change.
Engineers from our AI Quality Engineering practice bring that skill set into your team. Their first job is usually the hardest one: getting the organisation to agree what a correct output actually looks like, and building a golden dataset from real traffic that encodes it. Everything else, regression, thresholds, monitoring, depends on that dataset existing.
How we vet, and why it is not a CV screen
Every Engineer is assessed by our Senior QA leadership before they reach a client, and the assessment is practical rather than biographical.
Every Engineer is assessed by our Senior QA leadership before they reach a client, and the assessment is practical rather than biographical. Candidates work through problems from real engagements: diagnose why this suite is flaky, design coverage for this feature under time pressure, review this test code and say what you would change. A CV tells you what someone has been near. A working session tells you how they think when the answer is not obvious.
We also screen for the things that decide whether an embedded Engineer succeeds: whether they write a defect report a developer can act on, whether they push back when a requirement is ambiguous, and whether they can explain a technical risk to a product owner without either patronising them or hiding behind jargon. Those matter more than tool familiarity, which is learnable in a fortnight.
What the role owns
Golden datasets built from real inputs with agreed expected outputs, versioned alongside the product so quality has a definition that survives staff changes.
Evaluation harnesses scoring accuracy, grounding, refusal behaviour, latency and cost per change, typically in Python, running in your CI.
Prompt regression, so a prompt edit is a reviewable engineering event with a measurable effect rather than a hopeful change to a string.
RAG pipeline evaluation: retrieval recall, chunking strategy, grounding enforcement and citation accuracy, plus behaviour when retrieval returns nothing relevant.
Agent and tool-use validation: task completion, wrong-tool selection, argument validation, permission boundaries, and loop and cost limits.
Adversarial coverage: prompt injection, jailbreak attempts and hostile inputs, tested deliberately rather than discovered by users.
How the commercials work
Engagements are billed as a monthly rate per Engineer, with no separate recruitment fee and no placement charge.
Engagements are billed as a monthly rate per Engineer, with no separate recruitment fee and no placement charge. You interview the person we match before anything starts, and you can decline, matching is our job, not a fait accompli. An NDA is available before you share any details, and everything the Engineer produces lives in your repositories under your ownership.
Typical time to start is days after a scoping call rather than the weeks or months a hiring process takes, because we are matching from an existing team rather than opening a search. If the engagement needs to end, it ends with a handover rather than a cliff. Full commercial detail, including how project and fractional models compare, is on the pricing page.
Engagement path
From first call to embedded Engineer
Four steps, typically days rather than the weeks a hiring process takes.
Scoping call
What the AI feature does, which model and pattern it uses, what 'correct' currently means, and how quality is judged today. Usually the answer is spot-checking.
Matching
Matched on the pattern you are shipping, RAG, agents, extraction or chat, since the evaluation approach differs materially between them.
You interview
A technical conversation against your actual feature. Declining costs nothing and matching honestly is the point.
Dataset first, then gates
A golden dataset from real traffic, a harness that scores it, then CI thresholds so quality regressions fail a build like any other defect.
Deliverables
What the engagement includes
Everything the Engineer produces lives in your repositories and your tooling, under your ownership.
Handover pack
6 artefacts · yours to keep
A named AI Testing Engineer embedded in your product team
A versioned golden dataset built from your real traffic
An evaluation harness scoring accuracy, grounding, refusal, latency and cost
CI thresholds that fail builds on meaningful quality regression
Adversarial and prompt-injection coverage, plus empty-retrieval behaviour
Production output sampling and drift detection your team can run
Self-check
Signs you need this role
If more than one of these is true, it is usually cheaper to fix now than after the next release.
An AI feature is live and quality is judged by spot-checking outputs
Prompt changes ship with no regression signal at all
Nobody can say whether the last model update improved or degraded the product
Token spend is unpredictable and unowned
Your QA team is excellent and has never been asked to test something non-deterministic
A Senior Engineer replies within one business day. NDA first.
Questions
Frequently Asked Questions
Straight answers, written the way we'd say them on a call.
Still curious? Talk to usKeep reading
Where to go next
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- A Senior Engineer replies, not a sales layer
- Within one business day, every time
- NDA available before you share any details
16+
Years QA leadership
16
Testing disciplines
6
Markets served
1
Business day to reply
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