With the goal of expanding our team to 150-200 employees in 2027, Trivita AI is looking for talented individuals to join us in our next phase of growth.
Trivita AI is hiring Junior and Middle Automation QA/QC Engineers for web, mobile, API testing, test automation, and AI quality evaluation.
Trivita AI builds AI-powered products and platforms for businesses, including AI assistants, workflow automation agents, knowledge search, and speech recognition.
As a Middle/Junior Automation QA/QC Engineer, you will ensure product quality across web, mobile, desktop, APIs, and AI features. The role focuses on three areas: risk-based compatibility testing across real-world devices and platforms, test automation, and AI quality evaluation through golden datasets, data labeling, and model version comparisons.
Job description
Test analysis and design
- Analyze user stories and business requirements, identify gaps, inconsistencies, and ambiguities, and propose testable acceptance criteria.
- Design test cases and scenarios using standard techniques such as Equivalence Partitioning, Boundary Value Analysis, Decision Tables, and State Transitions.
- Apply end-to-end testing principles across UI, API, backend, and database layers, assessing the impact of changes on related workflows.
- Build and maintain compatibility testing matrices based on real usage data, device market share, operating systems, browsers, and screen sizes.
Cross-platform testing and automation
- Perform functional and regression testing for web, mobile, and API applications.
- Develop automated E2E tests across Chrome, Edge, Firefox, and Safari using Playwright, Cypress, or Selenium, including verification on real Safari environments.
- Test iOS and Android applications using physical devices, emulators/simulators, and device farms with Appium, Flutter integration_test, or Patrol.
- Validate mobile features such as permissions, camera, microphone, push notifications, deep links, biometrics, app upgrades, and offline behavior.
- Test localization, Vietnamese text rendering, input methods, date and number formats, and compatibility across Windows, macOS, and Linux.
- Perform API testing using Postman, Swagger/OpenAPI, or pytest, including schema validation, HTTP status codes, SQL checks, and service mocking.
- Integrate automated tests into CI/CD pipelines, manage flaky tests, automate repetitive tasks, and support load testing.
AI quality evaluation
- Build and maintain test sets and golden datasets for AI model evaluation.
- Perform data labeling and review according to labeling guidelines, ensuring accuracy and consistency.
- Evaluate AI outputs using metrics and quality rubrics, including WER/CER for speech-to-text and response quality assessment for chatbots.
- Test multi-step AI agents for issues such as incorrect tool selection, execution order errors, infinite loops, hallucinations, and basic prompt injection.
- Analyze failure cases, identify error patterns, and provide feedback to AI engineers.
- Retest models and engines after releases and compare results across versions to detect quality regressions.
Quality management and collaboration
- Document clear, reproducible bugs with supporting evidence and track them to resolution in Jira.
- Monitor quality indicators such as defect leakage and automation coverage.
- Participate in Scrum ceremonies, estimate assigned tasks, and collaborate with developers, product managers, and AI engineers.
- Maintain accurate test results, bug logs, datasets, and test cases while contributing to continuous QA process improvement.
Requirements
Experience and core skills
- Junior: 6 months to 2 years of software testing experience.
- Middle: 3+ years of software testing experience, including at least 2 years developing automated tests.
- Solid understanding of STLC, bug life cycle, test types, test levels, and test design techniques.
- Strong systems thinking and the ability to assess the impact of changes across end-to-end workflows.
- Experience with API testing using Postman or Swagger, including JSON, HTTP methods, status codes, and basic SQL.
- Experience testing iOS and Android applications using real devices, emulators/simulators, and debugging tools.
- Basic understanding of AI model evaluation, multi-step AI agent testing, and common agent failure patterns.
- Experience with AI data labeling or evaluation, or the ability to learn quickly.
- Familiarity with Git and Docker, and the ability to read English technical documentation.
- Proficiency in AI coding assistants and prompt engineering for generating test cases, writing automation scripts, debugging, and improving productivity while validating AI outputs and complying with data security policies.
Level-specific technical requirements
Junior
- Basic programming skills in Python or JavaScript/TypeScript.
- Ability to write simple scripts using pytest or requests for batch API calls and metric calculation.
- Ability to perform functional, regression, and compatibility testing across defined device matrices.
- Ability to label and review AI datasets and report reproducible bugs with supporting logs.
Middle
- Proficiency in at least one web E2E automation tool, such as Playwright, Cypress, or Selenium.
- Experience developing automation frameworks using Page Objects, fixtures, and stable locators.
- Experience integrating automated tests into CI/CD pipelines, enabling parallel execution, and managing flaky tests.
- Basic Kubernetes knowledge, including using kubectl to inspect logs and test environments.
- Ability to design AI evaluation sets, quality rubrics, and labeling guidelines while mentoring Junior engineers.
Technology stack
- Web Automation: Playwright, Cypress, Selenium.
- Mobile Testing: Appium, Flutter integration_test, Patrol, BrowserStack, LambdaTest.
- API Testing: pytest, requests, Postman, Newman, Swagger/OpenAPI, WireMock.
- Performance Testing: k6, Locust.
- AI Evaluation: Label Studio, promptfoo, DeepEval.
- Infrastructure & CI/CD: Git, Docker, Kubernetes, GitHub Actions, GitLab CI.
- QA Management: Jira, Allure.
First 6 months expectations
- Automate critical user flows across the agreed browser and device compatibility matrix, with tests executed for every release candidate.
- Maintain a golden dataset and evaluation rubric for at least one AI feature, comparing results after each model or engine release.
- Track flaky test rates and keep them below the agreed threshold, such as 2%.
Preferred qualifications
- Experience testing speech-to-text products or using data labeling tools such as Label Studio or CVAT.
- Experience evaluating LLMs using promptfoo, DeepEval, Ragas, or equivalent tools.
- Familiarity with device farms such as BrowserStack, LambdaTest, Sauce Labs, or Firebase Test Lab.
- Experience with visual regression testing and accessibility testing based on WCAG 2.2.
- Basic security testing knowledge, including OWASP Top 10 or OWASP MASVS.
- Experience profiling mobile performance using Android Studio Profiler or Xcode Instruments.
- Experience working in Scrum or Agile environments.
- ISTQB Foundation certification.
Contact information
- Address: No. 01, Street 104, Quarter 3, Binh Trung Ward, Ho Chi Minh City
- Phone: [protected info]