MLOPS Data Science
3 days ago
Key Responsibilities:Develop, train, and validate predictive and analytical models using machine learning techniques.Collaborate with data engineers and business teams to define data requirements and success metrics.Deploy machine learning models into production using ML Ops best practices.Build automated pipelines for model training, testing, monitoring, and retraining.Optimize model performance and ensure scalability and reliability.Monitor model drift, performance degradation, and ensure continuous improvement.Implement CI/CD practices for machine learning workflows.Technical Skills Required:Strong proficiency in Python (Pandas, NumPy, Scikit-learn, PyTorch/TensorFlow).Experience with ML Ops tools/frameworks (MLflow, Kubeflow, Airflow, SageMaker, Vertex AI, or Azure ML).Good understanding of data engineering concepts (ETL, data pipelines, APIs).Hands-on with containerization and orchestration (Docker, Kubernetes).Knowledge of cloud platforms (AWS, GCP, or Azure) for ML model deployment.Strong background in statistics, machine learning algorithms, and model evaluation techniques .Familiarity with version control (Git) and CI/CD pipelines.Preferred Qualifications:Bachelor's/Master's degree in Computer Science, Data Science, Statistics, or related field.Experience in handling large-scale data pipelines and real-time model serving .Exposure to feature stores and automated model monitoring.Strong problem-solving, analytical, and communication skills.
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Senior MLops Engineer
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Data Science
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