Machine Learning Engineer
1 day ago
Prior experience working with Machine Learning Operations and Data Science is essential.
Responsibilities include deploying, monitoring, and scaling machine learning models on cloud platforms such as AWS or GCP. Additionally, building and maintaining continuous integration/continuous deployment pipelines for machine learning workflows using GitHub Actions, Jenkins, or cloud-native tools is necessary.
Containerization and orchestration of workloads using Docker and Kubernetes, as well as managing infrastructure via Terraform or CloudFormation, are also required skills.
Implementing model registries, feature stores, and observability using MLflow, Feast, Prometheus/Grafana is a key responsibility. Collaboration with data scientists to operationalize machine learning models for personalization, recommendations, natural language processing is also required.
Proficiency in programming languages such as Python, as well as machine learning frameworks like Scikit-learn, TensorFlow, and PyTorch, and data pipeline technologies like Airflow, Spark, and SQL are essential.
Bonus skills include experience with real-time machine learning serving, A/B testing, or media/recommender systems.
Nice-to-have skills include knowledge of experiment design and causal inference, as well as exposure to real-time machine learning serving technologies like KFServing, Seldon, or Ray Serve.
Education: Bachelor's or Master's degree in a relevant field.
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