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QA Architect - R01571456 en Brillio-2

Brillio-2 · Bangalore, Indien · Hybrid

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Description

QA Architect

Job requirements

Experience Range: With at least 7 years of quality assurance experience, including substantial hands-on work with data science and machine learning testing frameworks Key Responsibilities:
  • Design and implement automated testing strategies for AI and data science outputs, ensuring accuracy and reliability across models and pipelines
  • Develop and maintain robust evaluation and validation frameworks for backend and frontend components, leveraging statistical and machine learning techniques
  • Collaborate with data scientists and engineers to define test cases, hypotheses, and statistical metrics for model assessment and improvement
  • Integrate advanced statistical tests such as T-Test, Z-Test, and regression analyses into automated QA workflows to validate model performance
  • Utilize tools like Great Expectations, Evidently AI, and specific machine learning frameworks (TensorFlow, PyTorch, Sci-Kit Learn, CNTK, Keras, MXNet) to monitor, track, and report on model drift, anomalies, and forecast accuracy
  • Optimize testing processes for scalability and efficiency using Python, PySpark, R, and related technologies in large-scale data environments
  • Configure and manage testing infrastructure using platforms such as KubeFlow and BentoML to streamline deployment and evaluation cycles
  • Troubleshoot and resolve issues in automated testing pipelines, driving continuous improvement and high-quality deliverables
    • Required Skills:
  • Advanced proficiency in Python and PySpark for test automation and statistical analysis
  • Expertise in statistical testing methods including Hypothesis Testing, T-Test, Z-Test, and Regression (Linear, Logistic)
  • Strong experience with machine learning frameworks such as TensorFlow, PyTorch, Sci-Kit Learn, CNTK, Keras, and MXNet
  • Hands-on knowledge of Great Expectations and Evidently AI for data validation and monitoring
  • Proficiency in SAS and SPSS for statistical computing and analysis
  • Deep understanding of probabilistic graph models and classification algorithms including Decision Trees and SVM
  • Experience with forecasting techniques including Exponential Smoothing, ARIMA, and ARIMAX
  • Familiarity with distance metrics such as Hamming, Euclidean, and Manhattan Distance
  • Advanced skills in R and R Studio for statistical modeling and QA scripting
  • Experience configuring testing platforms such as KubeFlow and BentoML
    • Preferred Skills:
  • Experience automating evaluation pipelines for AI/ML in production environments
  • Expertise in integrating QA processes with CI/CD workflows and cloud-native architectures
  • Knowledge of emerging ML testing tools and frameworks beyond industry standards
  • Ability to develop custom statistical metrics for model evaluation
  • Experience with QA automation for distributed systems at scale
    • Desired Qualifications:
  • Bachelor's degree in Computer Science, Data Science, Statistics, Mathematics, or a quantitative discipline
  • Certification in Quality Assurance, Data Science, or Machine Learning (e.g., ISTQB Advanced Test Analyst, TensorFlow Developer Certificate)
  • Certification in statistical analysis tools or platforms (e.g., SAS Certified Specialist, SPSS Certification)
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