AI Testing Engineer

Job Description

. Role Overview: We are seeking a motivated Junior AI Testing Engineer to join our team. In this role, you will support the testing of AI models and pipelines, with a special focus on data ingestion into knowledge graphs and knowledge graph administration. You will collaborate with data scientists, engineers, and product teams to ensure the quality, reliability, and performance of our AI-driven solutions. 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 Requirements: Education: Bachelor’s degree in Computer Science, Information Technology, or related field. Experience: ideally 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. Show more Show less