QA Engineer

Role overview A hands-on QA role responsible for end-to-end quality across evolving data and AI products, including LLM-powered features, entity resolution workflows, and data pipelines. The position is suited for a self-directed tester who thrives in a fast-paced environment and wants to build automation frameworks from the ground up while partnering closely with product, data science, and engineering teams.

Responsibilities - Architect and execute end-to-end QA strategies and test plans tailored to AI/ML systems. - Build automation frameworks from scratch to validate data ingestion, transformation, model inference, APIs, and user interfaces. - Design and embed automated QA processes into CI/CD pipelines for rapid feedback and reliable delivery. - Develop innovative testing approaches for complex AI/ML behavior, including LLM output validation. - Collaborate with product managers, data scientists, and engineers to advocate for quality best practices. - Validate entity resolution workflows and data quality outcomes across pipelines.

Requirements - Experience architecting comprehensive end-to-end testing strategies for AI/ML or data products. - Track record building automation frameworks and integrating them into CI/CD pipelines. - Familiarity with validating LLM-powered features and handling non-deterministic outputs. - Comfort working across data, API, and UI layers. - Experience collaborating inside lean, cross-functional product teams. - Self-directed working style suited to fast-paced environments. - Python knowledge for test automation and data validation.

Nice to have - Familiarity with lakehouse platforms such as Delta Lake, MLflow, or related tooling. - Cloud platform experience with AWS or Azure for AI/ML deployments and data infrastructure. - Background in Master Data Management, entity resolution, or data quality concepts.