
Cloud Machine Learning Engineer
2 days ago
Prioritize the deployment and management of machine learning models on cloud platforms like AWS or GCP.
Key Responsibilities:
- Develop and maintain continuous integration and delivery pipelines for machine learning workflows using GitHub Actions, Jenkins, or cloud-native tools.
- Containerize and orchestrate workloads with Docker and Kubernetes, managing infrastructure via Terraform or CloudFormation.
- Implement model registries, feature stores, and observability using MLflow, Feast, Prometheus, or Grafana.
- Collaborate with data scientists to operationalize machine learning models for personalization and NLP.
- Stay proficient in Python, machine learning frameworks (Scikit-learn, TensorFlow, PyTorch), and data pipelines (Airflow, Spark, SQL).
- Bonus: experience with real-time machine learning serving, A/B testing, or media systems.
Nice-to-Have:
- Experience with media or recommender systems.
- Knowledge of experiment design and causal inference.
- Exposure to real-time machine learning serving technologies.
Why This Role?
This is a critical position that requires expertise in deploying and managing machine learning models. You will be responsible for ensuring seamless model deployment, scalability, and reliability. Your skills in cloud computing, containerization, and DevOps practices will make you an ideal candidate for this role.
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