Senior AI QA Engineer: .NET + Python; short NP-, kindly

Position: Senior AI QA Engineer: .NET + Python(Only short NP-less than 30 days accepted, kindly pay attention
About the job
Attention please！

• Only short NP-less than 30 days accepted, kindly pay attention to this to save your efforts, thank you for understanding

Responsibilities:
Manual Testing

• Analyze requirements, acceptance criteria, and specifications for new features.

• Create and maintain test plans, test cases, and test scenarios across UI, API, and data workflows.

• Perform functional, regression, exploratory, and integration testing.

• Validate correctness of data transformation, analytical workflows, and large dataset processing.

• Collaborate closely with Product, Developers, and Data/AI Engineers to clarify expected behavior.
Automation

• Develop and maintain UI automation using:
o Selenium
o Playwright

• Implement E2E scenarios covering cross-service workflows.

• Integrate tests into CI/CD pipelines (Git Hub Actions / Git Lab / Jenkins / AWS Code Build).

• Configure test runs in AWS environments (dev/stage).

• Participate in root-cause analysis of defects and quality incidents.
Quality Engineering

• Contribute to QA strategy and test automation roadmap.

• Improve test stability, reduce flakiness, optimize runtime.

• Define KPIs for coverage, quality, and regression scope.

• Support test data preparation and environment setup.

Mandatory Skills

Description:

• Strong experience (5+ years) in C#/.NET test automation

• Strong Python skills (5+ years experience)

• UI automation:
Selenium / Playwright

• API automation:
Rest Sharp / Http Client

• Experience with Graph Databases (Graph DB, SPARQL or Cypher)

• SHACL validation experience

• SQL proficiency

• Experience testing data lakehouse tech (Iceberg)

• RAG/LLM QA experience (prompt testing, embeddings validation)

• CI/CD pipeline automation (Docker, Git Hub Actions/Git Lab/Azure Dev Ops)

• Solid understanding of testing methodologies, QA lifecycle, and Agile processes.

• Ability to analyze complex workflows and large datasets.

• Strong communication skills and a proactive mindset.