Machine Learning Engineer
2 days ago
Prior experience working with machine learning operations and data science is essential for this role. Our team requires someone who can effectively deploy, monitor, and scale machine learning models on cloud platforms like AWS or GCP.
- Deploy, Monitor, and Scale: Machine learning models on cloud platforms using SageMaker, EKS, Lambda, Vertex AI, GKE, or Cloud Functions.
- Build 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 while managing infrastructure via Terraform or CloudFormation.
- Implement Model Registries, Feature Stores, and Observability: Using MLflow, Feast, Prometheus, and Grafana to improve model management and data insights.
Collaboration with data scientists to operationalize machine learning models for personalization, recommendations, and natural language processing tasks is a must. Additionally, proficiency in Python, machine learning frameworks (Scikit-learn, TensorFlow, PyTorch), and data pipelines (Airflow, Spark, SQL) is required.
- Bonus: Experience with real-time machine learning serving, A/B testing, or media/recommender systems.
- Nice-to-Have:
- Knowledge of experiment design and causal inference.
- Exposure to real-time machine learning serving (KFServing, Seldon, Ray Serve).
This position requires a Bachelor's or Master's degree in Computer Science, Engineering, or a related field.
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