AI Test Engineer

Ford Motor Credit Company is continuously evolving and innovating, committed to delivering smarter, more connected financial solutions that support Ford customers and dealers across the globe.

Ford Credit operates as a global financial services provider, supporting vehicle financing, leasing, and mobility solutions across multiple international markets. As part of Ford’s Enterprise Technology organization, our teams are focused on building scalable, data-driven platforms to enable intelligent decision-making and world-class customer experiences.

We are investing in advanced technologies, including AI/ML, to modernize our systems, improve operational efficiency, and unlock new capabilities across the business.

Required Qualifications

5+ years of experience in software testing (manual and automation)

Strong programming experience in Python (preferred) or Java

Hands-on experience with test automation frameworks (Selenium, Playwright, PyTest, etc.)

Experience testing APIs using tools such as Postman or REST Assured

Strong understanding of SDLC, STLC, and Agile methodologies

Experience working with CI/CD pipelines and automated testing workflows

Preferred Qualifications (Bonus Points)

Experience with cloud platforms (GCP, AWS, or Azure)

Knowledge of data engineering or ETL testing concepts

Experience with performance or load testing

Experience working in a global/onshore-offshore model

DISCLAIMER

You will play a critical role in ensuring the quality, reliability, and performance of Web applications and AI-driven applications supporting Ford Credit’s U.S. operations. In this role, you will work closely with software engineers and product teams to validate applications, test data pipelines, and build scalable automation frameworks.

Team Structure: This position is part of a globally distributed team.

Location: Based in Mexico, supporting U.S.-based stakeholders.

What You will Do:

AI/ML Testing & Validation: Design and execute test strategies to validate machine learning models, including model outputs, accuracy, and performance across various datasets

Test Automation Engineering: Build and maintain automated test frameworks for APIs, data pipelines, and AI-driven applications using tools such as Python, PyTest, Selenium, or Playwright

Data & Pipeline Validation: Validate data integrity across ingestion, transformation, and model input/output layers

Integration & API Testing: Perform end-to-end testing of microservices and APIs (REST/GraphQL), ensuring reliability and scalability

CI/CD & Continuous Testing: Integrate automated testing into CI/CD pipelines using tools such as Jenkins, Azure DevOps, or GitHub Actions

Defect Management & Debugging: Identify, document, and track defects, collaborating closely with engineering teams to resolve issues quickly

AI Quality & Risk Assessment: Contribute to testing practices around model bias, fairness, and performance monitoring

Cross-Functional Collaboration: Work closely with U.S.-based engineering and product teams to align on requirements, timelines, and delivery expectations