{"id":1956147,"url":"https://alion.io/job/brillio-qa-architect-r01571456","title":"QA Architect - R01571456","company":{"id":3063,"name":"Brillio","domain":"brillio.com","url":"https://alion.io/company/brillio","size_band":"501-1000","is_staffing_agency":false,"employer_type":"services","is_intermediary":false,"listed_via":null,"ats_vendor":"Lever","truth_index":{"grade":"B","score":76,"open_postings":43,"ghost_share":0,"stale_share":0.977,"repost_share":0,"time_to_fill_p50_days":29,"computed_at":"2026-10-22T06:10:10Z"}},"role":"Data Science","role_family":"Data Science","seniority":"staff","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Bengaluru, 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frameworks for backend and frontend components, leveraging statistical and machine learning techniques\nCollaborate with data scientists and engineers to define test cases, hypotheses, and statistical metrics for model assessment and improvement\nIntegrate advanced statistical tests such as T-Test, Z-Test, and regression analyses into automated QA workflows to validate model performance\nUtilize 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\nOptimize testing processes for scalability and efficiency using Python, PySpark, R, and related technologies in large-scale data environments\nConfigure and manage testing infrastructure using platforms such as KubeFlow and BentoML to streamline deployment and evaluation cycles\nTroubleshoot and resolve issues in automated testing pipelines, driving continuous improvement and high-quality deliverables\nRequired Skills:\nAdvanced proficiency in Python and PySpark for test automation and statistical analysis\nExpertise in statistical testing methods including Hypothesis Testing, T-Test, Z-Test, and Regression (Linear, Logistic)\nStrong experience with machine learning frameworks such as TensorFlow, PyTorch, Sci-Kit Learn, CNTK, Keras, and MXNet\nHands-on knowledge of Great Expectations and Evidently AI for data validation and monitoring\nProficiency in SAS and SPSS for statistical computing and analysis\nDeep understanding of probabilistic graph models and classification algorithms including Decision Trees and SVM\nExperience with forecasting techniques including Exponential Smoothing, ARIMA, and ARIMAX\nFamiliarity with distance metrics such as Hamming, Euclidean, and Manhattan Distance\nAdvanced skills in R and R Studio for statistical modeling and QA scripting\nExperience configuring testing platforms such as KubeFlow and BentoML\nPreferred Skills:\nExperience automating evaluation pipelines for AI/ML in production environments\nExpertise in integrating QA processes with CI/CD workflows and cloud-native architectures\nKnowledge of emerging ML testing tools and frameworks beyond industry standards\nAbility to develop custom statistical metrics for model evaluation\nExperience with QA automation for distributed systems at scale\nDesired Qualifications:\nBachelor's degree in Computer Science, Data Science, Statistics, Mathematics, or a quantitative discipline\nCertification in Quality Assurance, Data Science, or Machine Learning (e.g., ISTQB Advanced Test Analyst, TensorFlow Developer Certificate)\nCertification in statistical analysis tools or platforms (e.g., SAS Certified Specialist, SPSS 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