AI Testing Engineer

Role Overview:

Junior AI Testing Engineer

Key Responsibilities:

• AI Model & Pipeline Testing:

  Design and execute test cases for AI models and data pipelines, ensuring accuracy, stability, and fairness.

• Knowledge Graph Ingestion:

  Support the development and testing of Python scripts for data extraction, transformation, and loading (ETL) into enterprise knowledge graphs.

• Knowledge Graph Administration:

  Assist in maintaining, monitoring, and troubleshooting knowledge graph environments (e.g., Neo4j, RDF stores), including user access and data integrity.

• Test Automation:

  Develop and maintain basic automation scripts (preferably in Python) to streamline testing processes for AI functionalities.

• Data Quality Assurance:

  Evaluate and validate the quality of input and output data for AI models, reporting and documenting issues as needed.

• Bug Reporting & Documentation:

  Identify, document, and communicate bugs or issues discovered during testing. Maintain clear testing documentation and reports.

• Collaboration:

  Work closely with knowledge graph engineers, data scientists, and product managers to understand requirements and deliver robust solutions.

Requirements:

• Education:

  Bachelor’s degree in Computer Science, Information Technology, or related field.

• Experience:

  Ideally some experience in software/AI testing, data engineering, or a similar technical role.

• Technical Skills:

• Proficient in Python (must-have)
• Experience with test case design, execution, and bug reporting
• Exposure to knowledge graph technologies (e.g., Neo4j, RDF, SPARQL) and data ingestion/ETL processes

• Analytical & Problem-Solving Skills:

  Strong attention to detail, ability to analyze data and systems, and troubleshoot issues.

• Communication:

  Clear verbal and written communication skills for documentation and collaboration.

Preferred Qualifications:

• Experience with graph query languages (e.g., Cypher, SPARQL)
• Exposure to cloud platforms (AWS, Azure, GCP) and CI/CD workflows
• Familiarity with data quality and governance practices