
Advanced Machine Learning Engineer
5 days ago
As a critical team member, you will be responsible for developing and deploying machine learning models into production using best practices in MLOps. Your primary focus will be on building automated pipelines for model training, testing, monitoring, and retraining.
- Develop predictive and analytical models using advanced machine learning techniques.
- Collaborate with data engineers and business stakeholders 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.
You will work closely with cross-functional teams to design and implement efficient machine learning workflows, leveraging tools such as PyTorch, TensorFlow, and Scikit-learn.
Tech Requirements- 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.
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