Senior AI QA Engineer, NorthBay

India
May 25, 2026
Full Time

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Job Description

NorthBay is searching for a Senior AI QA Engineer who will work together with ML Engineers to perform testing of AI and ML systems on Azure and GCP and create automated testing systems and analyze data pipelines and deliver AI solutions that maintain superior quality throughout their entire development process.

Experience: 4–6+ years in software QA, test automation, and AI/ML validation

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Main Duties:

  • Define and execute test planning, test strategies, and detailed test cases for AI/ML systems.
  • Develop and maintain automated testing frameworks using Selenium and Pytest tools.
  • Perform API testing for RESTful services to ensure system reliability and performance.
  • Validate data pipelines, ETL workflows, and machine learning model outputs.
  • Conduct end-to-end testing across AI lifecycle including ingestion, training, inference, and deployment.

Essential Qualifications:

  • Candidate possesses extensive expertise in Software Quality Assurance together with test automation methods. 
  • Demonstrates advanced skills in Python programming together with Selenium and Pytest testing frameworks. 
  • Possesses practical skills needed for API testing together with building and maintaining continuous integration and continuous delivery systems. 
  • Understanding of data pipeline testing and regression testing while also handling defect tracking systems. 
  • Experience in using Git and other version control systems to work on projects with multiple developers. 
  • Collaborate effectively with Data Engineers and ML Engineers from different departments.

Preferred Skills:

  • Experience in machine learning model validation projects which involved measuring system accuracy and detecting drift while identifying bias.
  • Knowledge about different cloud platforms which include AWS, Azure, and GCP.
  • Basic knowledge of Docker and Kubernetes which are used to create containerized application environments.
  • Experience with testing methods used to evaluate system performance and capacity.
  • Knowledge about systems used to monitor data quality and frameworks that ensure data integrity.

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