Role Description Jalasoft is seeking an experienced Automation Tester Engineer with strong skills in Python, Pytest, and AI technologies to join our dynamic quality assurance team. In this role, you will be responsible for: - Designing, developing, and executing automated test scripts using Python and Pytest frameworks. - Leveraging AI tools to improve test coverage and defect detection. - Collaborating closely with developers, product owners, and other stakeholders to ensure high-quality software delivery. If you have a passion for automation and AI-driven testing solutions, we encourage you to apply. Qualifications - 7+ years of experience. - Experience working with Scrum framework. - Good English Communication. Requirements - Framework Development — Build and maintain shared Python/Pytest test frameworks and libraries consumed by teams across the org. - CI/CD Integration — Define reusable GitLab CI/CD patterns for automated testing that teams can drop in and trust. - Toolchain Ownership — Operate the testing toolchain (K6, GitLab CI/CD, Terraform-managed infra) and keep it available, functional, and documented. - AI-Accelerated Output — Claude Code required. - Expected to drive multi-file changes, test generation, and CI debugging through these tools — not just autocomplete. - Cross team collaboration experience with SDETs, Developers, and QAs. Benefits - Remote work. - 13 floating holidays. - 15 vacation days per year completed. - Good working environment. Company Description Every qualified candidate who meets the requirements outlined in the job description will be considered in this hiring process without distinction. Furthermore, Jalasoft is an equal opportunity employer. We wholeheartedly embrace our responsibility to make employment decisions without regard to race, age, marital or social status, national origin, disability, sex, gender identity or expression, or any other characteristic or group of candidates or employees unrelated to their qualifications and suitability for the position. Our management is committed to upholding this policy with respect.

• Shift quality left, properly. Help teams catch defects at the cheapest possible moment: during refinement and task definition, before a line of code exists. Join story-writing and design discussions, ask the questions that surface ambiguity and edge cases early, and coach teams to build testability and clear acceptance criteria into the work from the start. • Lift the whole testing pyramid. Work with developers across the full range, from unit and integration tests through to end-to-end and system testing. Help them write tests that are fast, deterministic, and worth trusting; rebalance suites that are top-heavy or flaky; and establish what to test at each level, and why. • Make pipelines fast and cheap. Treat CI as a product. Profile and optimise pipeline definitions, cut wasted compute, parallelise and shard test execution sensibly, and shorten feedback loops so teams get a trustworthy signal in minutes, not hours. Help them see the time and cost their pipelines actually incur, and bring both down. • Bring AI into the quality toolkit. Help teams get real value from AI-assisted development: generating and maintaining tests, accelerating code review, and shortening feedback loops, while keeping a critical eye on what these tools produce so quality and trust do not slip. You champion these tools and show teams how to use them well. • Mentor and enable. This is the heart of the job. Pair with engineers, run workshops, review test code and pipeline changes, and leave behind teams that hold a higher quality bar on their own after you have gone. Success is measured by how much better the team is, not by how much you personally produced. • Own quality strategy across the SDLC. Assess where a team or programme stands, agree a pragmatic plan to improve it, and drive that plan through delivery: test strategy, environments and test data, release confidence, and the metrics that tell everyone whether quality is moving in the right direction.

• Own the QA strategy end-to-end: from triaging risk to implementing automation • Design and implement a scalable QA process tailored for small, fast-moving teams • Set the quality bar for what's required to ship to production safely, even at higher cadence • Automate as much as possible, including integration, E2E, and regression testing across our Django and NextJS stack • Apply AI to QA — whether it's test case generation, diffing, log analysis, or something we haven't thought of yet • Collaborate closely with product engineers and prototypers to balance speed and safety

• Design, write, and execute test plans for new features, regression cycles, and edge-case scenarios across mobile (iOS/Android), web, and API • Catch issues in requirements and designs before engineering begins — not after • Maintain and improve test coverage documentation so the team always knows what's tested and what isn't • Triage and reproduce bugs with enough detail that engineers can act immediately — no back-and-forth • Participate in sprint planning and design reviews to identify gaps and risks early • Raise quality concerns through appropriate channels, with clear reasoning and suggested mitigations — not just "this feels wrong" • Monitor post-release behavior and surface regressions before customers do • Suggest improvements to QA processes, tooling, and test infrastructure when you see a better path • Test for what the spec says and what it doesn't — UX consistency, copy accuracy, accessibility, and real-world user flows • Cross-reference acceptance criteria against actual behavior meticulously, flagging ambiguities rather than making assumptions • Use Claude or other AI tools to generate comprehensive test cases from requirements, PRDs, or acceptance criteria — then apply your own judgment to refine and extend them • Prompt AI tools to identify edge cases, boundary conditions, and failure scenarios that might not be obvious from the spec alone • Use AI to validate expected behavior by asking it to reason through a user flow or business rule — surfacing logical gaps before testing even begins • Draft and iterate on bug reports, test plans, and coverage documentation with AI assistance to produce clearer, more thorough output faster • Stay current on how AI tools can improve QA workflows and bring new ideas to the team