Quality engineering for a specification-driven world

At Bosenet, software is increasingly built through a cycle of Understand → Specify → Design → Delegate → Generate → Review → Test → Validate → Improve .

Requirements are written as structured specifications and significant parts of the implementation are produced with AI coding agents — which changes what testing means. Quality can no longer be inspected in at the end of a sprint; it starts at the specification, where ambiguity is cheapest to remove and where a sharp question from a tester prevents an entire class of defects.

We are looking for a hands-on QA specialist who is comfortable in the room when specs are being discussed, who designs their own test strategy rather than executing someone else's, and who is evolving towards an AI-native development lifecycle (AIDLC).

What you will do

- Own the test strategy, methodology and quality approach for your products and integrations — you define it, you are not handed it.
- Take part in specification and design discussions, and ask the questions that expose ambiguity, missing acceptance criteria, unstated assumptions and edge cases before code is written; push back where a specification is not testable.

• Design and maintain automated suites with Playwright and/or Selenium — functional, regression, cross browser and end-to-end — plus API and integration automation for REST services, microservices, event driven workflows and SaaS integrations.

- Run sharp exploratory and risk-based manual testing where automation is not the right instrument.

• Wire tests into CI/CD pipelines so quality signals arrive continuously, not at release time.

- Review AI-generated code and AI-generated tests critically — correctness, coverage gaps, silent assumptions, and tests that pass without proving anything.
- Use AI assistants and agents for test design, test data, coverage analysis, failure triage and test maintenance, while keeping quality judgement under human control.
- Drive defects to root cause, and cover non-functional quality: performance, security-relevant behaviour, accessibility, reliability and observability.
- Communicate quality status, risk and release readiness clearly to engineers, architects and international customer stakeholders.

What we are looking for

• Minimum 5 years of hands-on professional testing and QA experience on real, delivered software.





- Demonstrable ownership of test strategy, test design techniques and methodology — not only test execution.

• Strong automation experience with Playwright and/or Selenium : framework structure, locator strategy,

synchronisation, parallel execution and flake control.

- Solid programming ability in at least one automation language (Java, TypeScript/JavaScript, Python or C#), and practical API testing experience (REST, JSON, auth, contract testing).
- Comfortable with source control, CI/CD pipelines, and how modern applications are built — APIs, microservices, databases, cloud-native deployment — enough to test them below the surface.

• Strong spoken and written English , for working with international teams and clients.

- The temperament of a valuable colleague: curious, direct, collaborative, willing to challenge a spec or a design respectfully and with evidence — and a strong personal sense of ownership for what ships.

Working in an AI-native development lifecycle (AIDLC)

We do not expect years of experience testing agent-generated software — very few people have it. We do expect you to understand why it changes the job, and to want to master it: reading and interrogating specifications produced with GitHub Spec Kit, BMAD or comparable methodologies; treating a weak specification as a quality defect in its own right; using Claude Code, Cursor, GitHub Copilot, Codex or similar to accelerate test design and triage; knowing the characteristic failure modes of AI-generated tests — plausible assertions that verify nothing, missing negative paths, over-mocking, silent scope drift from the spec; and judging what is safe to delegate to an agent versus what a human must design. We care more about how you use AI to do quality engineering than which tool you have used.

Additional advantages

- Production use of AI assistants or agents in a QA workflow
- Testing AI, GenAI or agentic solutions (non-deterministic output)
- Performance and load testing (JMeter, k6, Gatling)




- Security-oriented testing / OWASP Top 10
- Contract testing, mocking, service virtualisation
- BDD and living documentation (Cucumber, SpecFlow)
- AWS, Microsoft Azure or Google Cloud Platform
- Docker, Kubernetes, containerised test execution
- Event-driven architecture and messaging technologies
- Accessibility (WCAG) and cross-browser/device strategy
- Mobile test automation (Appium or equivalent)
- ISTQB or comparable certification
- Direct experience with international customers
- Knowledge of the German language
- Test data management at scale

Why Bosenet?

• Agentic engineering — AI agents as a normal part of delivery

• Spec-driven development — structured requirements, not vague tickets

• Modern architecture — cloud-native, microservices, integrations, APIs

• International exposure — customers in Germany and beyond

• Quality leadership — grow into test architecture and QA strategy

• Emerging AI solutions — AI and agents inside what we deliver

Eligible employees may also receive opportunities for client-site assignments in Germany, depending on project requirements, performance and applicable travel and visa conditions.

Compensation

₹ 8–20 Lakhs CTC per annum . The range reflects the breadth of profiles we will consider.

Compensation depends on relevant QA experience, depth of automation capability, programming and technical depth, ownership of test strategy, cloud-native and integration testing experience, AI-native testing maturity, communication and client-facing capability, and overall fit for the responsibility level. Exceptional strength in automation architecture, cloud-native quality engineering or AI-native testing is positioned towards the higher end.

How to apply

Send us your resume , and briefly describe one real project where you owned the testing approach :

- What was being built, and what were the biggest quality risks?
- How did you design the test approach — and why that approach? What did you automate, with which tools, and what did you deliberately not automate?
- Which questions did you ask during requirements or specification discussions that changed the outcome?
- Have you used AI tools in your testing work? How — and how did you verify what they produced?
- What measurable difference did your work make to quality, escape rate or release confidence?

📌 QA Specialist / SDET (Kolkata)
🏢 Bosenet Systemhaus GmbH u0026 Co.
📍 Kolkata