Quality Engineer, AI & Test Automation
Skills
Artificial IntelligenceQuality AssuranceTest Automation
What the job involves
The main requirements, responsibilities and hiring steps.
Requirements
- Bachelor’s degree in Computer Science Engineering Information Systems Data Science or related technical field
- 5–8+ years of experience in quality engineering software testing test automation SDET software engineering AI quality or related technology delivery roles
- Hands-on experience designing and implementing automated tests for APIs web or mobile applications workflows data validation integrations or end-to-end product experiences
- Experience working with engineering teams in agile DevSecOps CI/CD or modern product-delivery environments
- Experience with defect triage root-cause analysis test evidence documentation release readiness and quality metrics
- Exposure to AI-enabled products machine learning systems large language models conversational experiences agentic workflows automation or decision-support technologies preferred
- Strong understanding of software testing concepts including test design test coverage functional testing regression testing integration testing data validation exploratory testing and release validation
- Hands-on experience with test automation frameworks scripting test data CI/CD integration and defect management tools
- Ability to validate APIs backend services user interfaces data flows workflows and enterprise integrations
- Programming scripting or data analysis skills preferably Python JavaScript TypeScript Java SQL or comparable languages
- Familiarity with AI-enabled testing and evaluation concepts including prompt response validation model-output evaluation RAG or retrieval testing agent workflow testing hallucination risk guardrails and human-in-the-loop workflows
- Ability to use logs traces telemetry monitoring dashboards test artifacts and production feedback to investigate quality issues
Nice to have
- automation-first
- analytical
- collaborative
- detail-oriented
- high-trust environment
Day to day
- Designs, builds, executes, and maintains automated test suites, validation utilities, evaluation assets, and quality reporting across digital products and AI-enabled use cases.
- Partners with Product, Engineering, Architecture, Data & Analytics, AI Foundation, Security, Privacy, Clinical, and Operations teams to embed quality early in the delivery lifecycle.
- Uses automation, API validation, data validation, CI/CD integration, defect triage, and AI-enabled testing practices to improve release confidence, speed, reliability, and quality insight.