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Proficiency with cloud technologies such as AWS, Azure, and Google Cloud Platform; familiarity with serverless architectures, Docker, and Kubernetes. Exposure to CI/CD/DevOps, Terraform, Docker and monitoring technologies.
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As a Full Stack SME, you will play a pivotal role in building UI, middleware and backend as well as upstream/downstream system integrations, hands-on development, unit testing, and Google Cloud Platform (GCP) deployment of multiple microservices based workflow and reporting applications.
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Spring boot, Microservices, Docker, Jenkins and REST API's experience. Watch the below videos of us participating at Industry events with the Top companies in Technology at Oracle Cloud world /Oracle Java one (Las vegas) and at Gartner Data Analytics Summit (Florida.
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Expertise in cloud computing platforms (Azure, AWS, Google Cloud) and containerization technologies (Docker, Kubernetes). Expertise in cloud computing platforms (Azure, AWS, Google Cloud) and containerization technologies (Docker, Kubernetes.
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Experience with cloud development platforms (e.g., Azure, AWS, Google Cloud) and a solid understanding of cloud-based architectures. Knowledge of containerization technologies like Docker and container orchestration platforms such as Kubernetes.
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Familiarity with cloud platforms (e.g., AWS, Azure, Google Cloud) and containerization (e.g., Docker, Kubernetes). Knowledge of Infrastructure as Code (IaC) principles and automation tools like Terraform.
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Cloud Computing: AWS (EC2, S3, RDS, Lambda), Azure, Google Cloud Platform, IAM, EC2 Auto Scaling, Amazon Redshift, CloudFormation. Role: AWS cloud Engineer. DevOps & Monitoring: Docker, Jenkins, Ansible, Terraform, Prometheus, Grafana.
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Experience with cloud platforms such as AWS, Azure, or Google Cloud. Strong understanding of containerization technologies such as Docker and Kubernetes. Experience with cloud platforms such as AWS, Azure, or Google Cloud.
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Experience with Cloud/PaaS environments such as AWS, Azure, and Google Cloud Compute. Familiarity with emerging containerization technologies like Docker, Rancher, Kubernetes, and OpenShift.
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Experience with Docker/Kubernetes, cloud technologies (AWS, GCP, Azure), and on-prem/cloud deployments. We sync to all sources of information (Slack, Google Drive, Confluence, Github, etc.
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Azure/ AWS/ GCP certifications, AWS Machine Learning Specialty, Google Certified Cloud Engineer and Deeplearning.ai certifications on LLMs, prompt engineering. Deployment and publish for AI and ML services with ACR, ACI, Docker, Azure Kubernetes.
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Active contributor to one or more PAAS platforms software such as, Snowflake, Databricks, Confluent, Google BigQuery, Spanner, Redshift, Aurora, Cosmos DB, Redis, MongoDB, ElasticCloud, CockroachDB, Yugabyte, Hashicorp, etc.
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Strong knowledge of cloud platforms such as AWS, Azure, or Google Cloud. Experience with containerization technologies (e.g., Docker, Kubernetes) The ideal candidate should have a strong background in cloud technologies, such as AWS or Azure, and experience in system administration and networking.
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O Familiarity with cloud platforms, such as AWS Sagemaker, Azure ML, or Google Cloud vertex. o Proficiency in containerization technologies (e.g., Docker, Kubernetes). o Expertise in working with cloud-based infrastructure.
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Knowledge of cloud platforms such as AWS, Azure, or Google Cloud. Familiarity with containerization and orchestration tools (e.g., Docker, Kubernetes). Your expertise in Python, PySpark, ETL processes, CI/CD (Jenkins or GitHub), and experience with both streaming and batch workflows will be essential in ensuring the efficient flow and processing of data to support our clients.
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