MLOps Engineer
Коротко о вакансии
Компания Inizio Partners Corp ищет MLOps Engineer для удаленной работы. Специалист будет разрабатывать и поддерживать end-to-end пайплайны MLOps для развертывания, мониторинга и масштабирования моделей машинного обучения. В задачи входит сотрудничество с дата-сайентистами, инженерами и DevOps, внедрение CI/CD, автоматизация тестирования моделей и оптимизация развертывания с помощью контейнеризации. Кандидату необходим опыт работы с Kubernetes и Kubeflow, а также знание Python и MLOps инструментов.
- Формат работы
- удалёнка
- География
- США, удалённо (Dallas)
- Зарплата
- 125 000 $ — по данным Himalayas
- Ключевые требования
- Опыт работы MLOps Engineer от 5 лет
- Опыт с Kubernetes и Kubeflow обязателен
- Владение Python для ML пайплайнов
- Опыт с MLOps инструментами: Kubeflow Pipelines, TFX, MLflow
- Знание Docker и Kubernetes для развертывания моделей
- Кому подойдёт
- Вакансия подойдёт опытному MLOps инженеру, который хочет работать удалённо в американской компании и имеет глубокие знания Kubernetes и Kubeflow.
Пересказ openqareer по тексту работодателя. Ниже — оригинал объявления.
Job Title: MLOps Engineer
Job Description:
We are seeking a highly skilled and experienced MLOps Engineer to join our client. As an MLOps Engineer, you will play a crucial role in developing and implementing machine learning operations processes and infrastructure to support our data science initiatives.
Responsibilities:
- Develop and maintain end-to-end machine learning operations (MLOps) pipelines for deploying, monitoring, and scaling machine learning models.
- Collaborate with data scientists, software engineers, and DevOps teams to ensure seamless integration of ML models into production systems.
- Design and implement automated testing frameworks for ML models to ensure accuracy, reliability, and performance.
- Optimize model deployment processes by leveraging containerization technologies such as Docker or Kubernetes.
- Implement continuous integration/continuous deployment (CI/CD) practices for ML model development lifecycle management.
- Monitor deployed ML models in production environments to identify performance issues or anomalies.
- Work closely with cross-functional teams to troubleshoot issues related to model performance or data quality in production systems.
- Stay up-to-date with the latest advancements in MLOps toolkits, frameworks, best practices, and industry trends.
Requirements:
- Bachelors degree in computer science or a related field; advanced degree preferred.
- Minimum 5 years of experience working as an MLOps Engineer or similar role within a data-driven organization.
- Experience with Kubernetes and Kubeflow is mandatory.
- Strong understanding of machine learning concepts and algorithms.
- Proficiency in Python developing ML pipelines/scripts.
- Experience with popular MLOps toolkits such as Kubeflow Pipelines, TensorFlow Extended (TFX), MLflow, etc., is essential.
- Solid knowledge of containerization technologies like Docker and Kubernetes for deploying ML models at scale.
- Familiarity with cloud platforms like AWS/Azure/GCP for building scalable infrastructure solutions is highly desirable
- Experience with version control systems like Git/GitHub for managing code repositories
- Excellent problem-solving skills with the ability to analyze complex technical issues related to ML model deployments.
Location:
Remote/Dallas.
Originally posted on Himalayas
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