Job Title

Job DescriptionWe are looking for an ML Engineer focused on Data and Model Validation to ensure the correctness, reliability, and production readiness of machine learning systems. This role owns data quality, model evaluation, drift detection, and release validation for ML pipelines deployed at scale.This is not a traditional QA role — it is an engineering role with ownership of ML quality.Experience - 2 yearsKey Responsibilities·Own data validation and quality checks for ML pipelines (training & inference)·Define and implement baseline expectations on datasets and features·Validate ML models using metrics such as F1, precision, recall, AUC·Perform regression testing across model retraining cycles·Detect and respond to data drift, feature drift, and prediction drift·Implement automated quality gates that block or allow model releases·Integrate validation checks into CI/CD and MLOps pipelines·Validate batch and real-time inference systems·Define model release readiness criteria·Document validation logic and monitoring thresholdsMandatory Skills·Hands-on experience with Great Expectations or Deequ·Strong Python skills (Pandas, NumPy, PyTest)·Strong understanding of ML fundamentals·Experience validating ML models in production·Experience with ML pipelines·Familiarity with CI/CD pipelinesGood to Have·Experience with Scikit-learn, TensorFlow, or PyTorch·Experience with MLflow, Azure ML, SageMaker, or Vertex AI·Experience with LLM / GenAI systems·Cloud experience (AWS / Azure / GCP)What This Role Is Not·Not a UI testing role·Not manual QA·Not Selenium-based testingHiring NoteCandidates must demonstrate hands-on experience validating ML data and models using Great Expectations or equivalent tools.