Lead QA Engineer

Job Description

**Description**
**At this time, we are unable to offer visa sponsorship for this position(H1b/OPT). Candidates must be legally authorized to work for any employer in the United States (or (applicable country) on a full-time basis without the need for current or future immigration sponsorship**
****QA Engineer (AI/ML)**
*(Exempt)***
Enterprise AI/ML Organization

Reports to Leader of ML Engineering Group

**Overview**
This QA Engineer position is for a hands-on professional with experience in testing and automating AI/ML pipelines. The ideal candidate is someone who has worked closely with machine learning engineers and data scientists to ensure the quality and reliability of AI/ML models and systems. You will join a dynamic team passionate about innovation, learning, and applying cutting-edge technologies to deliver high-quality AI solutions.

**Responsibilities**

– Develop and implement QA strategies tailored for AI/ML solutions, including models, APIs, pipelines, and agent-based architectures.
– Create and maintain automated and manual test cases for model validation (accuracy, bias, robustness, explainability, drift).
– Collaborate with AI engineers, data scientists, and product teams to define success criteria, acceptance standards, and performance metrics.
– Validate model outputs and system behaviors against business and ethical guidelines.
– Perform regression, integration, stress, and adversarial testing of AI models and systems.
– Identify, log, and track bugs and anomalies, ensuring timely resolutions.
– Support monitoring production AI systems to detect model performance degradation (concept drift, data drift, hallucinations).
– Ensure compliance with internal AI governance standards, responsible AI principles, and regulatory requirements.
– Contribute to building automated AI testing frameworks, pipelines, and synthetic data generation systems.
– Document testing procedures, results, and quality assessments clearly and effectively.

****Must Haves:****

– Bachelor’s degree in Computer Science, Engineering, or a related field.
– Minimum 6 years of experience in quality assurance, specifically testing AI/ML applications.
– Experience with the following:

– Hands-on skills with Python and relevant AI/QA libraries (Pytest, Unittest, Great Expectations, MLflow, Deepchecks, etc.).
– Familiarity with machine learning frameworks (TensorFlow, PyTorch, or scikit-learn).
– Experience with test automation tools and frameworks.
– Knowledge of CI/CD tools (Jenkins, GitLab CI, or similar).
– Experience with containerization technologies like Docker and orchestration systems like Kubernetes.
– Familiarity with version control systems like Git.

– Strong understanding of software testing methodologies and best practices.
– Excellent analytical and problem-solving skills.
– Excellent communication and collaboration skills.