Platform Engineer

Posted 2024-11-07

💎 Seniority level: Junior, 2+ Years

📍 Location: Turkey

🔍 Industry: Digital Transformation & IT Consulting

🗣️ Languages: English

⏳ Experience: 2+ Years

🪄 Skills: PythonBashKafkaKubernetesCollaboration

Bachelor's Degree in Computer Engineering/Science, Electrical & Electronics Engineering or equivalent fields.
Minimum 2+ Years of Big Data Systems Administration experience.
Knowledge of containerization technologies such as Kubernetes and Openshift (preferred).
Hands-on experience in implementing data warehouse/lake/lakehouse.
Experience with Cloudera CDH/CDP installation, configuration, monitoring, cluster security, resource management, maintenance, and performance tuning (nice to have).
Knowledge of Bash and Python scripting for basic automation.
Good knowledge of Linux OS.
Passionate about learning big data, new technologies, and open source.
Creative and innovative problem-solving skills.
Team player with a solution and customer-oriented attitude and analytical mindset.
Advanced level of English.

Posted 2024-11-28

📍 Turkey

🔍 Digital Transformation & IT Consulting

Bachelor's degree in Computer Engineering, Software Engineering or related field.
Min 2+ years experience of provisioning and maintaining Kubernetes clusters, especially for on-prem systems.
Hands-on experience on Kubeflow is a reason for preference.
Good knowledge about bash scripting.
Good knowledge of version control systems (Git, etc.).
Advanced knowledge of Linux system administration.
Good to have experience on monitoring applications (Grafana, Prometheus etc.).
Good to have experience on configuring and managing code-based infrastructure (Ansible, Puppet etc.).

Collaborate with data science and engineering teams to streamline the development, deployment, and management of machine learning models.
Create & maintain Kubernetes clusters in large scale environments (dev/test/staging etc.).
Implement automated processes for model training, testing, and deployment using tools such as Kubernetes, Kubeflow, and Docker.
Develop and maintain robust data pipelines to support the end-to-end machine learning lifecycle.
Ensure data security, privacy, and compliance with regulatory requirements throughout the machine learning pipeline.

DockerBashGitKubeflowKubernetesMachine LearningData scienceCollaborationLinuxCompliance

Posted 2024-11-28

🔧 Requirements