Machine Learning QA Engineer

Info Resume Edge; Sharjah; On-site; AED 200,000 - 300,000; Full time

Job summary

A leading company specializing in machine learning solutions is seeking a detailed-oriented Machine Learning QA Engineer. This role involves ensuring the quality and reliability of ML models through comprehensive testing strategies, collaborating with teams to validate data pipelines, and maintaining high standards throughout the ML lifecycle. If you are experienced in QA for data products and possess strong Python skills, we encourage you to apply.

Qualifications

• Proven experience in QA/testing of machine learning systems.
• Strong understanding of software testing practices.
• Experience with Python and testing frameworks.

Responsibilities

• Design and implement test strategies for ML pipelines.
• Develop automated testing frameworks for validating ML models.
• Collaborate with ML engineers to ensure model deployment.

Skills

Understanding of software testing practices

Experience with Python

Familiarity with ML frameworks

Experience testing RESTful APIs

Familiarity with CI/CD pipelines

Education

Bachelors/Masters degree in Computer Science

Degree in Data Science

Degree in Engineering

Tools

PyTest

TensorFlow

Scikit-learn

Git

Jenkins

Job description

We are seeking a detail-oriented and technically skilled Machine Learning QA Engineer to ensure the quality accuracy and reliability of our ML models and systems. You will work at the intersection of quality assurance and machine learning developing automated and manual test strategies validating data pipelines and verifying ML model performance against business objectives.

Key Responsibilities:

• Design and implement test strategies for machine learning pipelines APIs and models.

• Develop automated testing frameworks for validating ML model inputs outputs and performance.

• Collaborate with ML engineers data scientists and DevOps teams to ensure seamless model deployment.

• Evaluate data quality and integrity throughout the ML lifecycle.

• Test for accuracy bias fairness reproducibility and drift in models.

• Validate and monitor ML models in production environments.

• Write detailed test plans test cases and quality documentation.

• Participate in code reviews and contribute to QA best practices in ML workflows.

Requirements:

• Bachelors/Masters degree in Computer Science Data Science Engineering or related field.

• Proven experience in QA/testing of machine learning systems or data products.

• Strong understanding of software testing practices (unit integration system regression).

• Experience with Python and testing frameworks (e.g. PyTest unittest).

• Familiarity with ML frameworks (e.g. TensorFlow PyTorch Scikit-learn).

• Understanding of data validation tools (e.g. Great Expectations TensorFlow Data Validation).

• Experience testing RESTful APIs and backend systems.

• Familiarity with CI/CD pipelines and version control systems (e.g. Git Jenkins).

Preferred Qualifications:

• Experience with MLOps tools like MLflow Airflow or Kubeflow.

• Exposure to cloud platforms (AWS/GCP/Azure) for model deployment and monitoring.

• Knowledge of model interpretability explainability and ethical AI principles.

• Experience working in Agile/Scrum teams.