About Upshop:
Upshop is the foremost provider of a SaaS platform designed to streamline forecasting, ordering, production, and inventory optimization processes for food retailers. Its unified platform simplifies and enhances associate tasks, promoting smarter and more interconnected operations across Fresh, Center, DSD, and eCommerce departments. With over 450+ retailers and 50,000+ stores relying on its mission-critical operations platform globally, customers have witnessed substantial enhancements in sales, shrinkage reduction, food safety, and sustainability throughout their stores.
At Upshop, we believe that great businesses are built by great people. Our People function is at the heart of our company’s growth, ensuring we attract, develop, and retain A Players who drive our mission forward.
Overview of the Role:
We are looking for a Senior Quality Engineer who owns quality, reliability, and performance through strong automation—and who uses AI as a force multiplier, not a substitute for judgment.
You build and maintain end-to-end and API coverage, contribute to test strategy, and shift quality left with agents, RAG, and prompt engineering. You stay accountable for what those systems produce. You advocate for quality, act before you are asked, and challenge weak process, thin coverage, or “this is how we’ve always done it” when it puts the customer at risk. You adapt to a fast-paced engineering culture: changing priorities, short cycles, and delivery without lowering the quality bar.
Key Responsibilities
- Design, build, and maintain E2E tests (Cypress, WebDriverIO) and API tests (Karate) in JavaScript/TypeScript / Node.js.
- Pick the right layer: prove rules and data in API tests; use UI/mobile only when that surface is the proof.
- Run suites, analyze failures, drive defects to close, and keep pipelines trustworthy (flakes, failing jobs, follow-up until done).
- Shift left: review acceptance criteria for testability, work from specifications and scenarios, and map every criterion to API, Web, and/or Mobile—or mark it not applicable with evidence. Do not invent tests from a ticket title.
- Keep tests requirement-traced from design through automation. CI/release results are the source of truth for pass/fail.
- Use AI agents to design cases, implement automation, and raise reviewable PRs that extend existing frameworks. Apply prompt engineering and RAG so output is grounded in specifications, existing tests, and standards—not guessed.
- Review and correct agent output. Stop when evidence is missing. Never claim AI work passed without pipeline proof and human review. Protect secrets and customer data in prompts and retrieval.
- Own team quality outcomes: coverage, automation standards, PR gates, and release readiness—not just assigned tickets.
- Advocate for quality in planning, design, and reviews. Speak up on risk, gaps, and untestable stories. Question the status quo and propose a better path.
- Stay effective when scope, priorities, or timelines move. Re-plan coverage, communicate risk early, and keep automation and pipelines usable under delivery pressure.
Required Skills
- 7–8+ years of experience in Quality Engineering (QE) or as an SDET.
- Hands-on Cypress, WebDriverIO, Karate; strong JavaScript/TypeScript and Node.js frameworks.
- Experience building and maintaining automation frameworks and core testing practices.
- Practical use of AI coding agents, with prompt engineering and retrieval-backed (RAG) workflows; judgment to accept, revise, or stop generated work.
- Shift-left mindset: early testability, requirement-traced automation, human accountability for AI-assisted quality.
- Proactive, quality-advocate posture: owns outcomes, steps outside the comfort zone, and challenges weak defaults.
- Proven ability to adapt to a fast-paced engineering culture and deliver quality under changing priorities.
- Experience working in Agile/Scrum environments.
- Strong analysis, troubleshooting, and cross-team collaboration.
Nice to Have
- CI/CD integration of automated tests; performance/load tools.
- Spec-driven or agentic workflows; evaluating quality of RAG or conversational AI features.
