Senior Test Automation Engineer ; 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.

You will join our ML & AI ART as a Senior Test Automation Engineer, responsible for the technical test automation solution supporting our AI and MLOps platform. The platform powers AI-driven capabilities across the Bank including RAG systems, LLM-based assistants, agentic workflows, and ML pipelines serving business-critical use cases. You will own the architecture, implementation, and execution of our test automation frameworks, covering both traditional software components (APIs, UIs, data pipelines) and emerging AI/ML-specific behaviour, and act as the Test Automation Ambassador within the ART.

You will work within the Bank's Test Strategy, while owning the technical depth and hands-on delivery of automation for your platform.

• 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 Git Lab CI/CD pipelines, including merge request pipelines, Git Lab 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…