Machine Learning Infrastructure Engineer

2 weeks ago


palakkad, India beBeeMachineLearning Full time

Machine Learning Operations RoleThe primary objective of an ML Ops engineer is to deploy and scale machine learning models on cloud platforms. With a minimum of 2 years of experience, the successful candidate will be responsible for building and maintaining continuous integration and delivery pipelines using GitHub Actions or Jenkins.In collaboration with data scientists, the ML Ops engineer will work on operationalizing machine learning models for personalization, recommendations, and natural language processing tasks.The ideal candidate should possess proficiency in Python, machine learning frameworks such as Scikit-learn, TensorFlow, and PyTorch, as well as data pipelines like Apache Airflow, Spark, and SQL.Bonus points will be awarded to candidates with experience in real-time machine learning serving, A/B testing, or media/recommender systems. Knowledge of experiment design and causal inference, along with exposure to real-time machine learning serving, is also desirable.An ML Ops engineer should be skilled in containerizing and orchestrating workloads with Docker and Kubernetes, managing infrastructure via Terraform or CloudFormation, and implementing model registries, feature stores, and observability using tools like MLflow and Feast.



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