Aimed at experienced QA professionals with AI and TypeScript expertise who are comfortable with legacy system migrations and collaborative client engagement.
Derived from job-description analysis by Serendipath's career intelligence engine.
Original posting from Poland and Middle-Eastern Europe via Greenhouse
Xebia is a global AI-first, digital transformation, and engineering partner. With over 25 years of experience and a team of 5,000 professionals across 16 countries, we help organizations design and build scalable products, platforms, and data-driven solutions.
We specialize in Artificial Intelligence, Data and Cloud, Intelligent Automation, and Digital Products, combining deep technical expertise with a strong focus on engineering excellence and a people-first culture.
In the CEE region, we’re a team of nearly 1,000 experts delivering modern applications, data platforms, and AI solutions for clients such as Millennium, ING, Play, Edenred, Arabian Drilling, FedEx, Leroy Merlin, Truecaller, Volotea, Schmitz Cargobull, and many, many more. We work with leading technologies including AWS, Azure, GCP, Databricks, and Snowflake, and combine strong engineering culture with a consulting mindset and a continuous focus on growth and knowledge sharing.
We are looking for a Senior Quality Engineer to join a small, senior team modernizing a large portfolio of legacy desktop business applications into modern web solutions. The applications belong to an established financial software platform. Migration is carried out with AI coding agents inside a structured, gated workflow. In this role you will own quality and verification. You will define how we prove that a migrated application behaves like the original, build automated checks into the delivery workflow, and help the client’s team adopt the approach.
You will be:
designing the verification approach for migrated applications, from test strategy through acceptance,
helping define behavioural specifications and keep them testable and traceable to the source system,
building automated quality gates into an AI-assisted development workflow,
testing REST APIs and web front ends, including data validation against the legacy system,
measuring and reporting on test effectiveness, review effort and quality metrics,
running acceptance testing with domain experts and record results in agreed formats,
preparing and running knowledge transfer sessions for the client’s engineering team.
Your profile:
5+ years in quality engineering, including verification of legacy system migrations or rewrites, such as characterization testing, golden master testing or parity testing,
specification-based test design and traceability, for example with BDD, acceptance criteria or requirements-based testing,
strong test automation skills in TypeScript,
REST API testing, including contract and schema validation and data-driven tests,
web E2E testing, with Playwright preferred,
good SQL skills and experience validating data in business applications,
hands-on use of AI coding tools (such as Claude Code, Cursor or GitHub Copilot) in QA work, and the ability to critically evaluate AI-generated code and tests,
experience with mutation testing, fault injection or other methods of measuring how effective tests are,
testing data-intensive business systems, ideally in finance or ERP,
fluent English,
practical experience using AI-powered assistants (e.g. Claude Code, GitHub Copilot, Cursor) to improve productivity, quality, or decision-making in software delivery,
Work from the European Union region and a work permit are required.
Nice to have:
experience evaluating LLM output, such as eval harnesses or LLM-as-judge setups,
snapshot, approval, visual regression or property-based testing,
testing multi-tenant or single-tenant-per-customer deployments,
performance and batch processing testing,
mutation testing tools such as Stryker or PIT,
working knowledge of Docker and CI/CD pipelines, and setting up isolated test environments,
experience applying GenAI in a more structured way within the SDLC, including defined workflows, prompt patterns, or tool integrations embedded into daily work,
interest in and familiarity with emerging AI-driven practices (e.g. agent-based workflows, automation patterns, AI-augmented development), with a willingness to explore and experiment beyond standard approaches.
Recruitment Process:
CV review – HR call – Interview – Client Interview – Decision
Source: Poland and Middle-Eastern Europe careers (Greenhouse)