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.

Join our ML & AI ART as a Test Manager & Quality Engineer responsible for testing AI & ML solutions throughout their life-cycle. Focus will be on assessing the quality of developed solutions already in the development phase and give active feedback/collaborate with the ML Engineer teams.

You are the gatekeeper before a solution goes into evaluation by Model Risk Management and also before Go-Live. You will also own the testing and quality assurance of technical components that are provided by the platform team such as model APIs, agent repository and other components. implementation, and execution of our test automation frameworks, covering both traditional software components

YOUR CHALLENGE

• Ensure delivery quality and proper testing of AI use cases
• Define and evolve the technical testing approach and framework architecture for the ML & AI ART, aligned with the Bank's Test Strategy and Test Policy
• Design reusable, scalable testing patterns (page objects, API clients, test data builders) that other engineers across squads can adopt, ensuring technical consistency of testing 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
• Integrate and orchestrate automated tests in GitLab CI/CD pipelines, including merge request pipelines, GitLab runner
• 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
• Triage execution results, raise defects in Jira with evidence (logs, traces, screenshots, videos), and communicate quality signals to the squad and Product Owner
• Contribute actively to PI Planning, System Demos, Inspect & Adapt, and other SAFe ceremonies as part of the ML & AI ART
• Prepare test data, ensuring synthetic or anonymised data is used wherever possible to meet confidentiality expectations

YOUR PROFILE

• Hands-on experience integrating and executing tests for AI solutions and/or large scale data projects
• 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 workflow
• 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
• Collaborative team player with strong ownership takes automation problems from analysis to execution to resolution with minimal supervision
• Bachelor's or Master's degree in Computer Science, Engineering, or a related field
• Minimum 5-7 years with data & ML platforms and their Quality Assurance and/or Testing 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
• Strong communicator able to work effectively with engineers, Product Owners and Scrum Masters
• Good organisational skills, structured and reliable
• Fluency in English; German is a plus

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