인턴 MLOps 엔지니어 이력서 예시

“Extensive expertise in Python and machine learning libraries (e.g., TensorFlow, PyTorch, scikit-learn)”

“Extensive knowledge of distributed computing and big data technologies like Spark, Kubeflow, Airflow and SQL”

“Collaborate with Data Scientists and Engineers across the full ML lifecycle, including building and scaling ETL pipelines, deploying models into customer-facing applications, and enabling efficient model development through cloud infrastructure and tooling”

“Experience in MLOps or DevOps practices, including building and operating production ML systems using Docker, Kubernetes, CI/CD pipelines, Git-based version control, API development, model serving (batch and real-time), and automated testing frameworks”

“Support development and maintain monitoring, alerting, and automated testing frameworks to ensure the reliability, performance, and integrity of data pipelines, models, and infrastructure”

“Experience in MLOps or DevOps practices, including building and operating production ML systems using Docker, Kubernetes, CI/CD pipelines, Git-based version control, API development, model serving (batch and real-time), and automated testing frameworks”

“As MLOps Engineer II, you will focus on supporting cross-functional teams in designing, deploying, and operating machine learning solutions while building scalable infrastructure, tools, and best practices across the Machine Learning Engineering (MLE) ecosystem.”

“Bachelor’s degree in Data Science, Computer Science, Statistics, Applied Mathematics or equivalent quantitative field”

“Extensive expertise in Python and machine learning libraries (e.g., TensorFlow, PyTorch, scikit-learn)”