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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. Over 60% of production ML workloads run on AWS. AWS engineers on RemoteAI are vetted for 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. Over 60% of production ML workloads run on AWS. AWS engineers on RemoteAI are vetted for 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. Cost-naive AWS setups routinely produce $20K+ surprise bills.
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 typically delivers $5K–$30K in annual savings and closes security gaps — a high-ROI first engagement.
AWS Cloud Engineers on RemoteAI charge based on project scope and experience level. The following ranges reflect typical engagements for each type of project.
Post your role on RemoteAI and specify your AWS stack (SageMaker, EKS, Bedrock, Lambda, etc.). Vetted AWS developers on RemoteAI are screened for production AWS experience — real accounts, real costs, real deployments — not just certification knowledge. Mid-level AWS developers typically start within 1–2 weeks of posting.
Mid-level AWS cloud developers charge $85–$145/hr. Senior AWS architects with enterprise ML platform or Bedrock experience charge $145–$240/hr. For fixed-scope projects (e.g., SageMaker pipeline build, multi-account setup), expect $8,000–$60,000 depending on complexity.
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.