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Hire AI QA engineers

AI QA engineers for model evaluation, application testing, and regression coverage.

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300+software QA engineers
15+years of experience in QA
96%industry-leading retention rate
What brings you here

1. What's driving the need for QA people right now?

Trusted by

Abbott - DeviQA client
Compass - DeviQA client
BP - DeviQA client
Tipalti - DeviQA client
Descript - DeviQA client
Mimecast - DeviQA client

Why hire AI QA engineers from DeviQA?

DeviQA engineers combine model output evaluation with API, integration, and end-to-end testing. They define acceptance criteria, check responses against representative user requests, and trace failures through the application workflow so your team can prioritize fixes before release.

DeviQA assigns only middle and senior QA engineers to client projects.

Test leads have 8–12 years of experience managing QA teams, processes, and delivery.

DeviQA QA engineers have an average of six years of software testing experience.

All DeviQA QA engineers hold ISTQB Foundation Level certification.

Clients own all test cases, automation code, documentation, and other project IP created from the first day.

Rates, team costs, and invoicing terms are defined before the engagement begins.

Choose staff augmentation, a dedicated QA team, project-based delivery, or managed testing.

Written and spoken English proficiency is verified before an engineer joins the project.

14

DeviQA QA engineers work across 14 locations.

3+

Average employee tenure exceeds three years.

4.4%

DeviQA’s employee turnover rate is 4.4%, equivalent to approximately 96% employee retention.

3-7

Typical client projects last between three and seven years.

DeviQA’s AI QA competencies you can rely on

DeviQA engineers evaluate model outputs, test API and application logic, validate data flows and integrations, and build regression tests for critical AI workflows.

Core responsibilities

Define acceptance criteria for AI outputs and user workflows

Build evaluation datasets with representative inputs and known failure cases

Test correctness, completeness, groundedness, and instruction adherence

Validate retrieval, citations, and missing-information handling

Test tool calls, permissions, approvals, and resulting system changes

Maintain regression coverage across model, prompt, and application updates

Investigate failures and document evidence for developers

Establish quality gates and release-readiness reports

Engineering capabilities

Python and TypeScript test automation

API, integration, and end-to-end testing

Automated evaluation runners and output validators

Repeat-run testing for variable model behavior

CI/CD integration and test artifact tracking

Load, latency, and failure-recovery testing

Log and trace analysis across AI workflows

Controlled adversarial testing for prompt injection and data exposure

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DeviQA’s AI advantage

At DeviQA, we use AI to make testing smarter and simpler. Our ecosystem is built to deliver faster, smarter, and more cost-efficient results — so your team can do more in less time.

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AI-powered IDE assistant

Reduces test script writing time

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QA companion

Provides suggestions for test optimization and addresses gaps

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Automated code review

Flags unused variables, improper loops, and other common errors

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AI for API testing in Postman

Streamlines API test case creation and response validation

Features

Test case creation

Code review

Exploratory planning

Log analysis

without AI

6 hrs

3 hrs

2 hrs

2 hrs

with DeviQA AI

4 hrs (33% saved)

2 hrs (33% saved)

45 min (60% saved)

1 hr (50% saved)

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Hire AI test engineers to check the answer, the action, and the complete user workflow

Choose how to hire AI QA engineers from DeviQA

Dedicated QA team

A team focused on quality across your AI application. Engineers maintain evaluation datasets, automate regression checks, test integrations, and assess releases as your product evolves.

Best for:

  • Long-term ownership of AI testing

  • Establishing an evaluation process from scratch

  • Covering multiple models, integrations, and user workflows

Get started

QA staff augmentation

Add individual AI testing engineers to your existing team. They work within your process and address specific gaps in evaluation, automation, or release testing.

Best for:

  • Adding expertise in RAG, agents, or model evaluation

  • Extending existing automation with AI-specific checks

  • Supporting a defined release or testing initiative

Get started
Case studies

Partner with us:
see the difference

See all stories
Abbott - DeviQA client

Abbott Laboratories is a global healthcare giant

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Web app testing
Test automation
API testing
Dedicated QA team

  • 90%

    Test coverage

  • 1.6k+

    Test cases created

  • X18

    Faster regression testing run

Read customer story
Compass - DeviQA client

Compass is the first modern real estate platform

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Web app testing
Test automation
E2E testing
Load testing
Mobile testing
+2

  • 85%

    Test coverage

  • 2k+

    Test cases created

  • 2.5x

    Faster regression testing run

Read customer story
Arklign - DeviQA client

Arklign is a dental practice platform

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Web app testing
API testing
Dedicated QA team
Mobile testing
+2

  • 95%

    Test coverage

  • 5k+

    Test cases created

  • 3k+

    Number of critical bugs logged

Read customer story
Tipalti - DeviQA client

Tipalti is a payment automation platform

flag
flag
Web app testing
Dedicated QA team
DB testing
API testing
Performance testing

  • 12

    Years of cooperation

  • 100%

    Covered performance

  • 2x

    Faster regression testing time

Read customer story
Xola - DeviQA client

Xola is a booking system for tours and attractions

flag
flag
Web app testing
Test automation
Mobile testing
DB testing
Dedicated QA team

  • 90%

    Test coverage

  • 3.2k+

    Automation test scripts created

  • 1-2h

    Time of regression

Read customer story

How to hire AI QA engineers from DeviQA

Define the testing scope, meet suitable engineers, and establish ownership of the work.

01

Share your goals

Explain your application, architecture, known quality issues, and release requirements. Include examples of successful and failed outputs where available.

02

Choose the cooperation model

Select individual engineers to strengthen your team or a dedicated QA team to own a broader testing scope.

03

Interview and approve your AI QA engineers

Meet shortlisted candidates and discuss their approach to your specific evaluation and automation challenges.

04

Start working together

Your engineers join your workflow, review existing coverage, and agree on priorities, deliverables, and reporting.

Sample profiles of our AI QA engineers

Ivan

Senior AI QA Engineer

7+ years of QA experience

Tests AI-powered applications across model outputs, APIs, and user workflows. Builds evaluation datasets, automates regression checks, and investigates failures in retrieval and generation.

SENIOR AI QA ENGINEER

Built evaluation datasets for a knowledge assistant, covering answer correctness, source attribution, and handling of insufficient evidence;

Developed automated checks for structured outputs, required fields, and API responses using Python and pytest;

Tested retrieval and generation separately to distinguish missing source information from unsupported model claims;

Validated document permissions and checked that restricted content remained inaccessible through search and generated answers;

Compared prompt and model versions against a shared regression suite, documenting improvements and unresolved failures.

QA AUTOMATION ENGINEER

Developed API and end-to-end tests for document submission, background processing, and result review workflows;

Integrated regression suites into CI pipelines, attaching logs and test artifacts to failed runs;

Tested timeouts, retries, and interrupted requests to verify recovery without duplicate processing;

Created reusable fixtures and controlled test data for repeatable integration tests.

QA ENGINEER

Tested onboarding, account management, and document workflows across web applications;

Validated API responses and database records to investigate inconsistent application behavior;

Used exploratory testing to identify missing validation and unclear recovery paths;

Worked with developers and product managers to define acceptance criteria and reproduce defects.

B.S. in Computer Science

ISTQB Certified Tester Foundation Level

Training in Python test automation and machine learning evaluation

Programming languages:

Python, TypeScript, SQL

Automation frameworks:

pytest, Playwright

API testing:

Postman, REST APIs, OpenAPI

AI evaluation:

Dataset-based test runners, scoring rubrics, JSON Schema validation

CI/CD tools:

GitHub Actions, GitLab CI, Docker

Monitoring & reporting:

Grafana, Kibana, application logs and request traces

Test management:

TestRail, Jira, Confluence

Databases:

PostgreSQL, MongoDB

AI output evaluation

RAG and citation testing

API and end-to-end automation

Evaluation dataset design

Permission and negative testing

Regression analysis across model and prompt versions

Defect investigation and reproducible reporting

Oleksii

Lead AI QA Engineer

10+ years of QA experience

Defines test strategy for AI-enabled products and coordinates evaluation across engineering, product, and domain teams. Establishes release criteria, reviews coverage, and mentors engineers in AI testing and automation.

LEAD AI QA ENGINEER

Defined an AI testing strategy covering generated outputs, retrieval, tool execution, and application workflows;

Established scoring rubrics with domain reviewers and investigated disagreements between automated evaluations and human assessments;

Designed agent test scenarios for tool selection, argument validation, approval requirements, and execution limits;

Introduced release checks combining deterministic assertions with broader model evaluations;

Led failure reviews using retrieved passages, tool results, model configurations, and application traces;

Mentored QA engineers in evaluation design, test isolation, and documenting the limits of test results.

SENIOR QA AUTOMATION ENGINEER

Built API and integration suites for applications with asynchronous processing and external service dependencies;

Designed test environments with controlled service failures to verify timeout, retry, and fallback behavior;

Implemented load tests to measure response latency, error rates, and queue behavior under concurrent requests;

Standardized test data preparation and reporting across development and staging environments.

QA ENGINEER

Tested complex account, billing, and reporting workflows using functional, integration, and exploratory methods;

Validated backend operations through API requests and SQL queries;

Maintained regression suites and prioritized testing around business-critical scenarios;

Collaborated with engineering leads on defect investigation and release-readiness reviews.

M.S. in Software Engineering

ISTQB Certified Tester Foundation Level

Training in test automation architecture, performance testing, and AI evaluation

Programming languages:

Python, TypeScript, SQL

Automation frameworks:

pytest, Playwright, API test frameworks

AI evaluation:

Batch evaluation runners, rubric-based scoring, human-review workflows

Performance tools:

Locust, k6

API & integration testing:

Postman, OpenAPI, service mocks

CI/CD & infrastructure:

Jenkins, GitHub Actions, Docker

Monitoring & reporting:

Grafana, Kibana, OpenTelemetry traces

Test management:

Xray, Jira, Confluence

Databases:

PostgreSQL, Redis, MongoDB

AI test strategy and coverage planning

Evaluation methodology and reviewer calibration

Agent behavior and tool-use testing

Risk-based release assessment

Performance and failure-recovery testing

CI/CD quality gates

Cross-team coordination and QA mentoring

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Hire AI testing specialists to turn quality requirements into repeatable release checks

Questions & answers

An AI QA engineer tests model behavior and the software surrounding it.

Coverage can include:

  • Output correctness and completeness.
  • Retrieval quality and source attribution.
  • Tool calls and resulting actions.
  • Permissions and data access.
  • Application workflows and integrations.
  • Performance, recovery, and escalation.

The scope depends on whether the application uses generative models, predictive models, agents, or a combination.

Hire AI QA engineers when model behavior affects your product’s results and conventional software tests do not provide enough coverage. Typical triggers include inconsistent answers, unreliable extraction, incorrect tool actions, or frequent model and prompt changes. Early involvement helps establish evaluation data and acceptance criteria before release.

Look for AI QA engineers for hire who combine test engineering with structured evaluation.

Assess their ability to:

  • Turn requirements into measurable criteria.
  • Build representative test datasets.
  • Automate API and workflow checks.
  • Analyze variable model outputs.
  • Investigate failures across system components.
  • Explain the limits of their test results.

Domain knowledge is valuable when correctness requires specialist judgment.

AI testing includes outputs that may vary between runs or have multiple acceptable answers. Traditional assertions still work for permissions, schemas, calculations, and application state. Model outputs may also require scoring rubrics, repeated runs, and human review. A complete strategy combines these methods rather than applying one scoring approach to every component.
Yes. You can hire AI test engineers to assess existing coverage and prioritize improvements. Provide the architecture, current tests, known defects, and representative user requests. The initial review should identify unclear requirements, missing evaluation data, untested integrations, and gaps in logging. Start with failures that have the greatest impact on users or release decisions.

RAG testing covers retrieval, generation, and the complete application workflow.

Key checks include:

  • Whether search finds the required evidence.
  • Whether retrieved content respects permissions.
  • Whether answers are supported by the sources.
  • Whether citations reference the correct passages.
  • How the application handles missing or conflicting information.
  • Whether content updates and deletions reach the index.

Separating these checks helps locate the cause of an incorrect answer.

Yes. Hire AI testing engineers to verify agent decisions and the actions executed by connected systems. Testing should cover tool selection, argument validation, permissions, approval steps, retry behavior, and execution limits. For actions that modify data, use controlled environments and verify the resulting state. An agent saying that an action succeeded is not sufficient evidence of completion.
AI test engineers define acceptable behavior and assess results across representative cases and repeated runs. They combine deterministic checks with scoring rubrics where judgment is required. Model versions, prompt versions, generation settings, and evaluation inputs should be recorded. This helps distinguish ordinary output variation from a meaningful regression.
Yes. AI test engineers for hire can automate evaluation runs and connect results to release checks. Fast checks can run with routine code changes. Larger evaluation suites may run on a schedule or before release, depending on runtime and cost. Define blocking criteria carefully — variable outputs and unreliable evaluators can otherwise create unstable quality gates.
Hire AI testing specialists with relevant security testing experience when the application processes untrusted content or can access sensitive data and tools. The scope should include adversarial inputs, unauthorized retrieval attempts, and actions outside the user’s permissions. Tests must examine application controls as well as model responses. This work complements broader application security testing.
Release readiness should be measured against agreed acceptance criteria. The assessment should include critical workflow results, output quality, access controls, failure recovery, and performance. Known defects and uncovered scenarios should be documented. A single average score is insufficient — critical failures need explicit treatment even when overall results are strong.
No. Automated evaluators help scale assessment, but their scores need validation against reviewed examples. Human review remains useful for ambiguous cases, domain-specific correctness, and consequential decisions. Evaluator disagreements should be investigated, and scoring changes should be tracked separately from changes to the application.
Yes. You own the testing code, evaluation datasets, documentation, and frameworks we create for your project from day one. Third-party tools and source materials remain subject to their respective licenses and access terms.
Yes. You can start with one engineer and expand as the testing scope grows. An initial engagement can establish a baseline, address critical coverage gaps, and document the work needed next. Additional engineers or a QA lead can then take ownership of separate workflows, evaluation areas, or release responsibilities.
To hire AI QA engineers from DeviQA, share your application’s purpose, technical stack, known issues, and target timeline. We clarify the required expertise and cooperation model, then arrange candidate interviews. After selection, agree on access, onboarding, testing priorities, and initial deliverables.
DeviQA provides AI QA engineers on a dedicated basis: they join your team, work under your process, and all IP and code is delivered to your repository from day one. We have worked in software testing since 2010 and delivered 600,000+ man days for 300+ clients, with 300+ QA engineers on staff and a 96% engineer retention rate against an industry average of 80%. DeviQA is certified to ISO 9001:2015, ISO/IEC 27001 and ISO/IEC 20000-1, all engineers hold ISTQB Foundation Level certification, and clients rate DeviQA 5.0 out of 5 across 77 verified reviews on G2, Clutch and GoodFirms.