Role description
Job Description: QE Lead - AI Projects
Minimum 10 Years of Experience Required
The QE (Quality Engineering) Lead Developer will play a pivotal role in driving quality assurance and engineering excellence across AI-driven projects. This position requires a seasoned professional with at least 10 years of hands-on experience in AI solution development, test architecture, and QE leadership. The ideal candidate will be responsible for designing robust test strategies, mentoring teams, and ensuring the delivery of high-quality AI products.
Key Responsibilities
• Lead and manage the QE team in all phases of AI project development, from requirements analysis through deployment and maintenance.
• Design, implement, and maintain end-to-end test frameworks and automation solutions tailored for AI/ML systems.
• Establish and enforce best practices for quality engineering, code reviews, and CI/CD pipelines in AI environments.
• Collaborate with data scientists, developers, and product managers to define testable requirements and acceptance criteria.
• Develop and execute comprehensive test plans, including functional, performance, security, and reliability testing for AI models and data pipelines.
• Analyze test results, identify root causes of defects, and drive continuous improvement initiatives.
• Mentor and coach junior QE engineers, fostering a culture of quality and innovation.
• Stay abreast of the latest trends and advancements in AI testing methodologies, tools, and technologies.
Must-Have Skills
• Minimum 10 years of experience in quality engineering, with at least 5 years focused on AI/ML projects.
• Deep understanding of AI/ML model development, data pipelines, and deployment lifecycles.
• Strong expertise in test automation frameworks (e.g., Selenium, PyTest, Robot Framework) and scripting languages (Python, Java, etc.).
• Experience with CI/CD tools (e.g., Jenkins, Azure DevOps, GitLab CI) and version control systems (e.g., Git).
• Demonstrated leadership skills, including team management, project planning, and cross-functional collaboration.
• Excellent problem-solving abilities and a track record of driving quality improvements in complex technical environments.
Good-to-Have Skills
• Experience with cloud-based AI platforms (e.g., AWS SageMaker, Azure Machine Learning, Google AI Platform).
• Familiarity with MLOps practices and tools (e.g., MLflow, Kubeflow, TFX).
• Understanding of data privacy, security regulations, and ethical considerations in AI.
• Exposure to big data technologies (e.g., Apache Spark, Hadoop) and data engineering workflows.
• Certifications in AI, software testing, or relevant cloud technologies.
• Strong communication and stakeholder management skills.
