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Amazon Web Services (AWS) or Google Cloud Platform (GCP) administration experience. Hands on experience in deployment and management of routing, switching, wireless, and security appliance technologies using vendors such as Cisco, Palo Alto Networks, and Meraki.
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You will also be responsible in building healthy DevOps platform with policies and procedures around implementing and supporting our global hybrid cloud container strategies using Kubernetes deployed in Google Cloud Platform (GCP) and Amazon Web Services (AWS.
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Hands-on experience: The candidate will need to work with various network and security solutions, including traditional firewalls, NSX components, and cloud workloads (VMs, Kubernetes, bare metal.
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Experience with cloud-based data platforms like AWS, Azure, or Google Cloud. Hands-on experience with big data technologies such as Hadoop, Spark, or Kafka. As a Data Engineer, you will have the opportunity to make a significant impact on our data-driven initiatives and contribute to cutting-edge projects that shape the future of our industry.
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Proficiency in cloud technologies (e.g., AWS, Azure, Google Cloud) and hands-on experience with infrastructure as code tools (e.g., Terraform, Ansible). Relevant certifications such as AWS Certified DevOps Engineer, Certified Kubernetes Administrator, or equivalent, are a plus.
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Hands-on experience using IaC software tools (CloudFormation, Terraform, Azure Resource Manager, GCP Cloud Deployment Manager etc) and CI/CD tools (IDEs, GitLab, GitHuB, Jenkins, CircleCI, etc.
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Hands on experience on cloud services like AWS, Google cloud, azure, etc. Exposure to Linux, EC2 Security, EC2 Balances, Automation Tools, AWS CLI,S3, Cloud Watch & Cloud Trail, SSH, Docker, Git, MLAB, Jenkins, Circle CI, Nagios, Jmeter & Blazemeter are must.
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Hands on experience in Docker, Kubernetes, Amazon AWS Cloud products Strong DevOps/ CI/CD process architecture/engineering. Red Hat, AWS, Azure, Google, or IBM Cloud Certified Architect certification.
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Familiarity with cloud platforms such as Azure, AWS, or Google Cloud for testing within cloud-based data ecosystems. Hands-on experience with Databricks for data processing and testing.
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Hands-on experience with additional non-core AWS, Azure, or GCP cloud services. Advanced experience with public and private cloud platforms like AWS, Azure, and Google Cloud.
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Possess in-depth knowledge and hands-on experience with DevOps tools and technologies, including but not limited to GitHub, Jenkins, Terraform, Docker, Ansible, Kafka, AWS, Apigee.
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Strong understanding and hands-on experience with cloud platforms such as AWS, Azure, or Google Cloud Platform (GCP). They contribute to the vision for developing our modern data infrastructure on our Microsoft Azure cloud platform.
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Experience with machine learning platforms and distributed systems such as AWS sagemaker, Google Cloud Vertex AI, Azure ML Studio, Databricks, You are hands-on and have strong programming skills and background in one or more of the following AI/ML domains: deep learning, natural language processing (NLP), and analytics.
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Experience with cloud platforms (e.g., AWS, Azure, Google Cloud) for deploying and scaling AI systems. Hands-on experience in machine learning techniques, NLP algorithms and Optical Character Recognition (OCR.
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At least 5 years' experience utilizing cloud-based solutions (AWS, Azure, Google Cloud) You have hands-on experience developing data science solutions using open-source tools and cloud computing platforms.
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