Quality Assurance Engineer

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Particle41

WebsiteLinkedInAll Job Openings

51 - 200 employees

☁️ SaaS

🤖 Artificial Intelligence

🏢 Enterprise

SaaS • Artificial Intelligence • Enterprise

Particle41 is a company that specializes in providing expertise and solutions in technology development, data science, and DevOps. They offer CTO advisory services, acting as a partner to strengthen tech strategies and deliver robust software solutions tailored to their clients' needs. Particle41 emphasizes modernizing operations using cloud architecture, integrating software systems, and leveraging artificial intelligence to provide innovative digital products. They work closely with businesses to ensure on-time project delivery, scalability, and maintaining data security. Particle41 is particularly focused on helping businesses improve their competitive edge through strategic tech solutions and ongoing support.

📋 Description

• Engage with requirements and acceptance criteria before development begins, identifying gaps, ambiguities, and edge cases early. • Contribute to design and refinement discussions to flag testability and quality risks while they are still cheap to fix. • Collaborate with stakeholders to define test objectives, scope, and approach aligned to project milestones. • Write and maintain automated test suites spanning UI, API, contract, and integration layers using TypeScript and Puppeteer (or equivalent tools). • Contribute to framework improvements and scaling of automation coverage across the feature area. • Use AI tooling consistently for test design, test data generation, edge-case identification, and debugging and critically evaluate AI outputs before incorporating them. • Stay current with evolving AI testing tools and advocate for their adoption where they improve coverage or efficiency. • Run security testing tools and a11y checks; interpret and report findings, applying OWASP Top 10 and basic manual accessibility validation (keyboard, contrast, screen reader). • Maintain quality gates in the CI/CD pipeline and ensure automated suites run reliably on every build. • Monitor production alerts and dashboards post-deployment; own post-deploy validation to confirm releases meet quality expectations. • Document defects with detailed descriptions, reproduction steps, expected vs actual results, and severity classification. • Collaborate with development teams to investigate, troubleshoot, and verify defect resolutions against acceptance criteria. • Track, analyse, and report on feature-level quality metrics (defect escape rate, test coverage, automation pass rates); maintain well-organised test documentation for team and stakeholder access. • Translate quality findings into customer-impact language, what's broken for users, who feels it first, and what changes when we fix it -not just defect counts, test pass rates, or coverage numbers. • Actively participate in sprint planning, refinement, and stand-ups, raising quality and testability risks early in each cycle. • Partner with product managers, developers, and DevOps to ensure quality is built in across the delivery lifecycle, not bolted on at the end. • Support the onboarding of junior QA team members and share knowledge and best practices across the team.

🎯 Requirements

• Bachelor's degree in Computer Science, Engineering, or a related field with 3–8 years of experience in software quality engineering in a fast-paced development environment. • Hands-on experience with TypeScript, Puppeteer, and third-party API integration. • Demonstrated ability to write automated test suites across UI, API, contract, and integration layers. • Experience working in CI/CD environments; familiarity with maintaining quality gates and reading pipeline output. • Working knowledge of OWASP Top 10 and experience running security testing tools. • Experience with accessibility tooling and manual a11y validation techniques. • Strong understanding of SDLC, STLC, and agile/scrum methodologies. • Excellent written and verbal communication skills; ability to report quality risk clearly to technical and non-technical stakeholders. • Demonstrated use of AI tooling to support test design, data generation, or debugging.