AWS is the dominant cloud for production AI, with the broadest service catalog, the largest startup ecosystem, and mature ML infrastructure in SageMaker and Bedrock. New AWS engineers on RemoteAI are reviewed by our team before they go live; their profiles describe hands-on production experience with real AWS accounts and real costs, not just certification knowledge. They know how to build cost-efficient ML infrastructure, not just make it work.
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AWS is the dominant cloud for production AI, with the broadest service catalog, the largest startup ecosystem, and mature ML infrastructure in SageMaker and Bedrock. New AWS engineers on RemoteAI are reviewed by our team before they go live; their profiles describe hands-on production experience with real AWS accounts and real costs, not just certification knowledge. They know how to build cost-efficient ML infrastructure, not just make it work.
AWS engineers design and operate cloud infrastructure for AI and data systems. They architect and implement SageMaker training pipelines and inference endpoints, design S3 data lake architectures, build serverless AI pipelines with Lambda and Step Functions, manage ECS/EKS for containerized workloads, configure networking (VPC, PrivateLink), set up cost monitoring with Cost Explorer and Budgets, and implement infrastructure as code with Terraform or CDK. Many specialize in the AWS AI/ML stack: SageMaker, Rekognition, Comprehend, Bedrock, and Textract.
Finding the right AWS cloud engineer requires more than evaluating technical skills — it means identifying someone who ships production-ready work and understands your business goals. Here is exactly how to do it.
SageMaker (managed, faster to start, more expensive), EC2/EKS (full control, cheaper at scale, more engineering), Bedrock (no infrastructure, pay-per-token for LLMs). The right choice depends on your team's capacity to maintain infrastructure, not just the architecture diagram.
AWS costs scale sharply with ML workloads: GPU instances, data transfer, and SageMaker endpoint hours add up fast. Ask specifically about Spot instance training, Savings Plans vs. Reserved Instances, and how they monitor for cost anomalies.
Ask for a CDK, Terraform, or CloudFormation repo they have written for AWS. Engineers who deploy exclusively through the console cannot reproduce environments, cannot peer review infrastructure changes, and create brittle setups. This is a non-negotiable for production AWS.
If you already have AWS infrastructure, a structured audit covering IAM over-permissions, unencrypted data stores, unused compute, and cost optimization opportunities is a good first engagement: it shows where the money and the security gaps are before you build more.
Post your role on RemoteAI and specify your AWS stack (SageMaker, EKS, Bedrock, Lambda, etc.). New profiles are reviewed by our team before they go public, but AWS skills are not tested, so ask each candidate about a real account they ran: the costs, the deployments, what broke. That tells you more than a certification.
Senior AWS architects with enterprise ML platform or Bedrock experience charge more than mid-level AWS developers; fixed-scope projects such as a SageMaker pipeline build or a multi-account setup are priced by complexity. Rates depend on seniority, country and scope. Put a budget or rate range in your job post and ask candidates for their rate in the first message; RemoteAI takes no placement fee on what you pay them.
SageMaker (training, tuning, deployment), S3 (data lake storage), EC2 / EKS (custom compute), Lambda (serverless pipelines), Step Functions (workflow orchestration), ECR (container registry), and Bedrock (managed LLM APIs). IAM, VPC, and CloudWatch are foundational to all of these.
SageMaker is AWS's managed ML platform. It provides managed training jobs (with automatic infrastructure provisioning), hyperparameter tuning, feature store, model registry, inference endpoints, and MLOps pipelines. It reduces the infrastructure management needed to run ML at scale on AWS.
AWS Bedrock is a managed API for accessing foundation models (Anthropic Claude, Meta LLaMA, Mistral, Amazon Titan) without managing ML infrastructure. It provides a standard API for chat, embeddings, and agents, with enterprise security features including VPC endpoints and data residency controls.