
MLOPS Data Science
4 weeks 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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