AI Infusion QE Leader

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AI Infusion QE Leader

Location : : Chicago Onsite

As discussed below are the details.

An AI Infusion QE Leader is responsible for integrating and ensuring the quality of AI models within a company's products or services. This role involves leading a team of quality assurance professionals to test and validate AI-powered systems, ensuring they meet performance, reliability, and security standards.

Key Responsibilities : This role must be able to drive AI infusion across our QE footprint. Cannot be an AI generalist and must be able to drive across the Insurance value chain.

Here's a more detailed breakdown of the responsibilities :

1. Leading the AI Infusion QE Team :

• Team Leadership :

Lead, mentor, and guide a team of quality engineers specializing in AI integration. This includes setting goals, providing feedback, and promoting a collaborative work environment.

Develop and execute quality assurance strategies for AI-infused products, including test plans, scenarios, and automation frameworks.

Ensure the team has the necessary resources and tools to effectively test AI-powered systems.

2. AI-Specific QA Tasks :

Assess the performance of AI models, including accuracy, fairness, bias, and explainability. This may involve using various metrics and tools to evaluate model output.

Develop and execute comprehensive testing procedures for AI-powered systems, including integration testing, edge case scenarios, and adversarial testing.

Ensure the quality and integrity of data used to train and deploy AI models, including data cleaning, validation, and anomaly detection.

Work with development teams to integrate QA processes into the CI / CD pipeline for AI models.

Prioritize and track defects related to AI-infused systems, ensuring timely resolution.

3. Collaboration and Communication :

Work closely with AI developers, data scientists, product managers, and other stakeholders to ensure seamless integration of AI into products.

Communicate QA status, issues, and findings to relevant stakeholders in a clear and concise manner.

Stay up-to-date with the latest advancements in AI and QA, sharing this knowledge with the team.

4. Tools and Technologies :

Utilize various QA tools, including test automation frameworks, bug tracking systems, and performance monitoring tools.

Leverage AI-specific tools and libraries for model evaluation, bias detection, and adversarial testing.

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