The Role

You will take complete ownership of quality across an ecosystem that serves real-time retail audio, ad campaigns, and IoT hardware streams daily. The core technical challenge is ensuring absolute reliability across a fragmented architecture—spanning four web apps, Python services, edge devices, and mobile platforms. You will establish the testing framework from scratch, migrating manual verification into AI-assisted test automation that dictates production releases.

About the Product

The product is an in-store AI audio and ad-tech platform that streams dynamic music, live announcements, and location-targeted campaigns to physical retail spaces. It runs on a distributed architecture handling multi-tenant web portals, complex ad-auction engines, mobile interfaces, and custom hardware playback devices operating in low-reliability network environments.

Technology Stack: The backend is built in Python, while the web interfaces use React and TypeScript alongside a React Native mobile player. Infrastructure and edge environments rely on Raspberry Pi running embedded Linux, orchestrated via Docker Compose, git, and GitHub Actions for CI/CD. Test engineering leverages Claude Code and Cursor for AI-generated automation across Playwright, Pytest, and custom CLI/Bash tooling.

What You’ll Be Doing

• Establish the release gatekeeping strategy to independently approve or block production deployments across all platforms

• Convert manual hardware and software validation rules into automated regression suites using Claude Code

• Engineer end-to-end UI automation across four web applications using modern execution frameworks like Playwright

• Develop API integration tests validating real-time ad serving, playlist generation, and transactional event reporting

• Expand Docker Compose test environments to automate real-time streaming validation for Raspberry Pi edge devices

• Conduct physical edge-device testing to verify hardware power-loss recovery, local caching, and ad-insertion timing

• Author structured, edge-case-driven test documentation and step-by-step verification protocols in Notion

• Trace system failures directly through server logs, SSH sessions, and CLI diagnostics to deliver isolated bug reports

What We Expect

Must-have

• Computer Science degree or equivalent demonstrated depth through complex side projects, hardware builds, or competitive programming

• Advanced command-line proficiency (Linux CLI, SSH, process management, shell scripting, log analysis)

• Solid comprehension of distributed web architecture, API contracts, client-server interactions, and database behavior

• High degree of autonomy with a natural tendency toward systematic edge-case discovery

• Fluent spoken and written English with overlap for UTC+2 working hours

Nice-to-have

• Hands-on experience with AI-assisted software generation (Claude Code, Cursor)

• Exposure to embedded Linux, Raspberry Pi, or home-lab infrastructure

• Familiarity with test frameworks such as Pytest, Playwright, Vitest, or Cypress

Why This Role Is Worth Your Time

• Full authority over the release decision pipeline—your sign-off directly controls what goes to production

• Early adoption of modern AI-first engineering workflows where test generation is built via AI pair-programming tools

• Direct ownership over end-to-end software and hardware execution loops, giving you broad operational reach across web, API, mobile, and IoT systems