AI Test Automation Engineer 100% (f/m/d)

At Julius Baer, we celebrate and value the individual qualities you bring, enabling you to be impactful, to be entrepreneurial, to be empowered, and to create value beyond wealth. Let’s shape the future of wealth management together.

YOUR CHALLENGE

• Define and evolve the technical test automation approach and framework architecture for the ML & AI ART, aligned with the Bank's Test Strategy and Test Policy

• Design reusable, scalable test automation patterns (page objects, API clients, test data builders) that other engineers across squads can adopt, ensuring technical consistency of test automation across the ART

• Analyse and evaluate requirements, Features, and Stories for testability during PI Planning, Backlog Refinement, and Iteration Planning

• Derive test cases from technical and risk analysis of both functional and non-functional requirements (reliability, performance, security, usability, robustness), selecting appropriate test techniques and automation scope based on risk, coverage goals, and ROI

• Automate identified test cases using Python-based frameworks — Playwright-Python for UI, requests + pytest for APIs, Behave or pytest-bdd for BDD/Gherkin — applying clean code principles, reusability, readability, and stability

• Design and implement AI/ML-specific test cases: evaluation pipelines for LLM outputs

• Build and maintain contract tests (e.g. Pact) for platform APIs and microservice boundaries

• Integrate and orchestrate automated tests in GitLab CI/CD pipelines, including merge request pipelines, GitLab runners

• Leverage Docker and Kubernetes to provision isolated, reproducible test environments

• Plan, schedule, and trigger automated test executions across environments (DEV, INT, UAT, pre-PROD) — including regression suites, smoke tests, release executions, and on-demand runs tied to merge requests and PI milestones

• Monitor execution health, investigate and quarantine flaky tests, and maintain a low false-positive rate to keep quality signals trustworthy

• Triage execution results, raise defects in Jira with evidence (logs, traces, screenshots, videos), and communicate quality signals to the squad, Product Owner, and Test Manager

• Produce execution evidence (run metadata, artefacts, reports) suitable for audit and release governance, in line with the Bank's Test Policy and retention requirements

• Contribute actively to PI Planning, System Demos, Inspect & Adapt, and other SAFe ceremonies as part of the ML & AI ART

• Ensure end-to-end traceability from Jira Features and Stories → automated tests → defects → test results, leveraging the Bank's test management integration

• Author and maintain BDD scenarios in Gherkin, linked to acceptance criteria on Stories

• Collaborate closely with Product Owners, Scrum Masters, ML Engineers, MLOps Engineers, and Data Engineers across squads

• Align work with the Bank's Test Strategy, and reporting requirements, supporting audit-ready artefacts, traceability matrices, and test progress reporting

• Support root cause analysis of defects using logs and traces

• Act as Test Automation Ambassador within the ML & AI ART consulting squads on automation design, framework usage, tooling choices, and test data strategy

• Prepare test data, ensuring synthetic or anonymised data is used wherever possible to meet confidentiality expectations

• Design AI/ML-specific test datasets where needed

• Own test maintenance and refactoring in response to UI, API, model, or prompt changes, ensuring continued compliance with access controls and test environment policies

YOUR PROFILE

• Proven expertise in Python-based test automation: Playwright-Python (UI), Behave or pytest-bdd (Gherkin/BDD), requests + pytest (API/service validation practical equivalence to REST-Assured)

• Demonstrated ability to design and own test automation frameworks, not just write test scripts including reusable utilities and maintainability patterns

• Hands-on experience integrating and executing automated tests within CI/CD pipelines, ideally GitLab or equivalent enterprise platforms

• Experience with distributed test execution, parallelisation, flaky-test management, and modern reporting tools (Allure, pytest-html, or equivalent)

• Solid grasp of Git and version control workflows, clean code principles, and code review culture

• Working knowledge of Docker; familiarity with Kubernetes basics (jobs, namespaces)

• Exposure to testing AI/ML systems, or strong motivation to develop this expertise: evaluation of LLM outputs, handling non-deterministic responses, evals for RAG and agentic workflows

• Understanding of API design, microservices, event-driven architectures, and authentication layers

• Sound understanding of SAFe and DevOps principles; experience operating in an Agile Release Train is a plus

• Experience with Jira for Story/Feature tracking and test management integration (Xray, Agile Hive)

• Demonstrated end-to-end thinking — connecting user journeys, data flows, authentication layers, and system boundaries

• Comfortable working within an established Test Strategy, collaborating with Test Managers on execution planning, reporting, and compliance

• Demonstrated awareness of information security, data privacy, and compliant test data handling in regulated environments

• Collaborative team player with strong ownership takes automation problems from analysis to execution to resolution with minimal supervision

• Strong communication skills, able to work effectively with engineers, Product Owners, Scrum Masters, Architects, and Test Managers

• Strong organisational skills, structured, reliable

• Bachelor's or Master's degree in Computer Science, Engineering, or a related field

• Minimum 5–7 years in Test Automation with substantial hands-on Python experience, including demonstrated framework design, ownership, and test execution at scale (not only script-level contributions)

• Experience in a regulated environment (financial services, healthcare, pharma) strongly preferred

• Certifications in ISTQB (Foundation as baseline)

• SAFe (SP, SSM, or equivalent), or DevOps disciplines are a plus

• Exposure to AI/ML systems through testing, development, or applied projects is a strong plus; appetite to develop deep AI/ML testing expertise is essential

• Fluency in English; German is a plus

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