Role : Senior Mobile QA Engineer - AI-Augmented Testing

About the Role :

Are you naturally curious, sharp-eyed, and excited by the challenge of holding AI to a higher standard than the humans who built it? Do you treat test cases as a craft, AI-generated suggestions as drafts to be questioned, and the boundary between "looks right" and "is right" as the place your best work happens? If so, this role is for you.

We're looking for the next generation of mobile tester - a Senior Manual QA Engineer who treats AI like a power tool, not a magic wand. You'll join our Mobile team to own quality across the Driivz mobile experience on Android, iOS, and the Driver Portal (Web). You'll work as a peer to our developers and PMs, sit at the table during PI-week QA scoping, and use Claude Code daily - but your judgement, not your prompts, will be the deliverable.

Key Responsibilities :

- QA Driivz Mobile EV features such as charging, site discovery, payments, biometrics, history, profile, notifications etc. - across Android (Kotlin), iOS (Swift), and the Driver Portal web flows.

- Author and maintain Testmo cases per the Driivz Testing Guideline; link every case to its Jira story; keep Epic-level QA aggregate estimates honest.

- Participate in PI-week QA scoping: name your test types, environments, risk rating, and cross-flavour scope on every committed Epic (per the QA sub-gate criteria a-d).

- Review product specifications and identify test requirements before stories enter grooming.

- Validate AI-generated test artefacts - cases, scenarios, repro steps - and reject anything that doesn't survive critical review.

- Verify accessibility (TalkBack on Android, VoiceOver on iOS) and localisation (RTL, dates, currencies, numbers) on every shipped feature.

- Triage incoming bugs with clean repro, environment context, logs, and a justified severity.

- Partner with developers on risk-driven exploratory testing - the structured cases catch known unknowns; you find the unknown unknowns.

- Recommend improvements to product behaviour and user experience based on test findings.

What we're looking for :

- 4-7 years of hands-on mobile QA on shipped Android + iOS production apps.

- Strong native-platform awareness: Android API 24 - 34+ fragmentation, iOS 15+ background-refresh non-determinism, deep-link routing, push notifications, offline behaviour, biometric auth, lifecycle edge cases.

- Driver Portal / web QA experience - or a strong willingness to span. We don't split testers by platform; you cover the full driver journey.

- Multi-tenant / multi-flavour parametric testing. You don't write a case four times; you write it once and parametrise.

- Accessibility: TalkBack, VoiceOver, contrast ratios, touch-target sizing, keyboard navigation.

- Localisation: RTL handling, locale-dependent formatting.

- Risk-based test depth - you know when 30 cases is right and when 8 plus exploratory is right.

- Network conditioning (Charles or Proxyman); real-device labs and cloud labs (Firebase Test Lab, BrowserStack, or equivalent).

- Comfortable testing RESTful APIs and reading mobile network traces - both client- and server-side awareness.

- Bug triage with clean repro - steps, environment, logs, severity, blast radius. Strong analytical and root-cause skills under pressure.

- Excellent English communication, both verbal and written.

- Self-starter, quick learner, comfortable working independently across cross-functional teams.

AI-era skills we're hiring for (the differentiator) :

- Fluent with Claude Code as a daily tool. Skills, agents, plugins, MCP servers - you use them to accelerate test design, generate edge cases from a spec, and pressure-test acceptance criteria.

- Authoring Testmo cases from a spec with AI scaffolding, then editing with judgement. You know that AI-generated cases are starting points, not deliverables.

- You read AI-generated spec analysis and feedback the Testing Guideline section that applies - shaping the downstream test-case generation before it runs.

- You spot AI failure modes - hallucinated steps, false coverage, plausible-but-wrong assertions, over-generic cases. You treat AI output like a junior engineer's PR: verify before merge.

- You know when NOT to use AI - security-sensitive flows, brand-critical UX moments, exploratory sessions where serendipity is the value, anything with regulatory exposure.

- You'll test code that AI developer agents authored and AI PR-reviewer agents approved - you know how these agents fail, which seams they get wrong, and where regression risk concentrates after AI-authored changes.

- You communicate in chain vocabulary - gates, contracts, state-transitions, Epics, stories. You think in graded handoffs, not just features.

- The bar isn't uses AI tools. The bar is judges AI output well.