✓
🎬 Intro
View profile
Running ML workloads on AWS means navigating a catalog of over 200 services to find the combination that actually works at your scale and cost target. SageMaker, EMR, Lambda, ECS, EKS, Glue, Athena, Bedrock — the right architecture is not obvious, and the wrong one is expensive. Post your AWS job on RemoteAI and hire an engineer who has designed ML and data infrastructure on AWS for real production workloads, not just passed a certification exam.
Pre-vetted talent pool
These are the types of AWS engineers available on RemoteAI — all pre-vetted, confirmed profiles.
Which services are currently in use, what is missing or broken, and whether this is a new architecture design, cost optimization, or migration from on-premise or another cloud.
SageMaker, EMR, Glue, and EKS experience specific to ML and data engineering — not just general AWS web infrastructure engineers.
Look for candidates who can explain AWS service trade-offs in terms of cost, performance, and operational complexity — not just technical feasibility.
What you negotiate is what you pay directly to your AWS engineer.
Hiring a aws engineer remotely starts with a clear, well-scoped job post. This guide covers what to include, how RemoteAI's matching works, and what you can expect at each step of the process.
AWS engineers on RemoteAI who specialize in ML and data infrastructure work across the full cloud stack: designing ML training and inference pipelines on SageMaker (training jobs, SageMaker Pipelines, Model Registry, real-time and batch inference endpoints), building data engineering infrastructure using AWS Glue, EMR, and Athena, implementing event-driven ML architectures with Lambda, SQS, and EventBridge, containerized workload deployment on ECS or EKS with GPU node groups, and infrastructure-as-code using Terraform or CDK. Strong AWS ML engineers understand the cost architecture of each service — the difference between a real-time and asynchronous SageMaker endpoint is a cost decision as much as a technical one.
A well-structured job post attracts the right aws engineers and filters out irrelevant applications. Here is the exact process to follow on RemoteAI.
Which services are currently in use, what is missing or broken, and whether this is a new architecture design, cost optimization, or migration from on-premise or another cloud.
SageMaker, EMR, Glue, and EKS experience specific to ML and data engineering — not just general AWS web infrastructure engineers.
Look for candidates who can explain AWS service trade-offs in terms of cost, performance, and operational complexity — not just technical feasibility.
What you negotiate is what you pay directly to your AWS engineer.
Looking to browse talent directly?
View vetted AWS Engineer Jobs and contact them directly — no job post needed.
Specific services in scope (SageMaker, EMR, EKS, Glue, Lambda, Bedrock), whether this involves training, inference, or data engineering, infrastructure-as-code preference (Terraform, CDK), and cost optimization constraints.
SageMaker is faster to set up with built-in model registry, experiment tracking, and managed endpoints. EKS gives more control for teams that need fine-grained GPU scheduling, multi-tenant isolation, or custom model serving. SageMaker is the right default for teams without deep Kubernetes expertise; EKS is better for large-scale, long-running ML platforms.
Free. No listing fee, no commission on contracts.
AWS ML engineers on RemoteAI typically charge $85–$185/hour. SageMaker MLOps and multi-account landing zone specialists are at the higher end.
Sign up free and describe the AWS services, ML workload type, and infrastructure problem. RemoteAI matches your post to AWS engineers with relevant ML and data engineering experience. Most posts have a shortlist within 24 hours.