AI/ML Platforms QA Engineer

Job Description

This role is responsible for ensuring the quality and reliability of AI/ML platform integrations and deployment pipelines in a production environment. The QA Engineer will focus on backend, API, and data validation testing across multiple systems, ensuring that ML pipelines and integrations function correctly across services. This position will build and maintain automated test frameworks, perform end-to-end integration testing, and help establish quality standards and processes within a newly forming AI/ML platform team.

We are a company committed to creating diverse and inclusive environments where people can bring their full, authentic selves to work every day. We are an equal opportunity/affirmative action employer that believes everyone matters. Qualified candidates will receive consideration for employment regardless of their race, color, ethnicity, religion, sex (including pregnancy), sexual orientation, gender identity and expression, marital status, national origin, ancestry, genetic factors, age, disability, protected veteran status, military or uniformed service member status, or any other status or characteristic protected by applicable laws, regulations, and ordinances. If you need assistance and/or a reasonable accommodation due to a disability during the application or recruiting process, please send a request to View email address on click.appcast.io learn more about how we collect, keep, and process your private information, please review Insight Global's Workforce Privacy Policy:

Skills and Requirements

• 6+ years of experience in QA, SDET, or software engineering

• Experience with white-box testing and ability to debug code for root cause analysis

• Strong experience with API and integration testing (REST-based services)

• Hands-on experience building automation frameworks using Python

• Experience with SQL and data validation (ETL or end-to-end testing)

• Experience testing distributed/cloud-based systems (AWS preferred)

• Experience testing end-to-end workflows across integrated systems in production environments • Experience testing ML pipelines or platform integrations

• Experience with Amazon SageMaker or Amazon Bedrock

• Experience with PyTest, Robot Framework, or similar tools

• Experience working with data platforms (e.g., Snowflake)