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At least 1 year experience with cloud computing (AWS, Microsoft Azure, Google Cloud) 3+ years of experience with AWS, GCP, Microsoft Azure, or another cloud service.
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4+ years of experience with a public cloud (AWS, Microsoft Azure, Google Cloud) Our CI/CD pipelines are implemented on top of Jenkins and deployed to AWS. Some of the other technologies we have in our ecosystem are Glue, S3, Lambdas, ALBs, Snowflake and Databricks.
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Hands-on experience with big data technologies and cloud (Databricks / Spark, Azure/AWS/GCP). Hands on experience utilizing both supervised and unsupervised ML (Machine Learning) algorithms.
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Knowledge of cloud computing technologies such as Hadoop, Apache Spark, AWS, Microsoft Azure, or Google Cloud. You will also be building advanced analytics solutions while leveraging various cloud technologies in partnership with the Tech Product Team and Data Scientists to robustly scale up AI/Machine Learning models.
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Comprehensive understanding of machine learning and NLP algorithms, with hands-on experience in frameworks like Scikit-Learn, TensorFlow, and PyTorch. Proficient in cloud platforms (AWS, Azure, GCP) with experience in deploying and leading complex AI/ML workloads.
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Expertise in LLM ecosystems, including platforms like Azure OpenAI, Google Vertex, and AWS Bedrock, and mastery of relevant tech stacks. Hands-on experience with LLM application frameworks, such as Langchain.
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Lead the development and implementation of critical platform components, including data pipelines, model training and serving infrastructure, API gateways, and monitoring systems, with a focus on innovation and efficiency.
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Hands on experience with cloud services such as AWS EKS, S3, EC2, Aurora / RDS, SQS, SNS, Batch, etc. Staff Software Engineer (Data & Machine Learning Operations.
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Knowledge and understanding of Cloud Service offerings on Azure or GCP related to security, data protection, and policy implementations. 3+ years of knowledge and understanding of Cloud service offerings such as data, analytics, AL/ML on GCP or Azure.
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4+ years of hands-on experience with GCP or Azure platform enablement and application migrations, build out of complex infrastructure programmable patterns using Infrastructure as Code (IaC.
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Microsoft Azure certifications, such as Azure Solutions Architect Expert or Azure DevOps Engineer Expert, are preferred. The Senior Cloud Engineer is responsible for designing, deploying, and administering cloud-based solutions in Azure.
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Experience with cloud-based technologies such as Azure DevOps or AWS. Expertise with cloud platforms (AWS, Azure). Experience with infrastructure as code tools (Terraform, CloudFormation, Azure Resource Manager, Google Cloud Deployment Manager.
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We enable organizations of all sizes to easily build, scale, and run modern applications by helping them modernize legacy workloads, embrace innovation, and unleash AI. Our industry-leading developer data platform, MongoDB Atlas, is the only globally distributed, multi-cloud database and is available in more than 115 regions across AWS, Google Cloud, and Microsoft Azure.
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Understanding and experience with Cloud architectures, specifically: Google Cloud Platform (GCP), Microsoft Azure, or Amazon Web Services (AWS) Atlas allows customers to build anywhere—on the edge, on premises, or across cloud providers.
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Hands on, demonstrable experience delivering technical solutions in the AWS space with a minimum of 5 years’ experience as an AWS Architect. Proven track record of designing and deploying scalable, highly available, and fault-tolerant systems on AWS. Experience documenting governance and workflows associated with cloud environments.
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machine learning hands on aws cloud microsoft azure jobs Title: data engineer
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