Skill specialists
Hire Vetted AWS Engineers for AI Infrastructure
Find cloud engineers who design, build, and maintain AI and ML infrastructure on AWS — SageMaker, Bedrock, ECS, and cost-optimised GPU training included.
Top matching AI talents
AI Specialization
Availability
Experience Level
Yuwetha
Amine
Muhammad Ishtiaq
Mangesh Lokade
Giuliana
Lilian
Shreyansh
Hiring guide
Why companies hire AWS experts
AWS is the most widely used cloud platform for AI and ML workloads. Engineers with AI-specific AWS expertise navigate SageMaker, Bedrock, Lambda, ECS, and the wider ecosystem to build scalable, cost-efficient AI infrastructure.
Popular projects
- Deploy and autoscale AI models on SageMaker endpoints
- Integrate AWS Bedrock LLMs into your product
- Reduce GPU training costs with Spot instances and Savings Plans
- Build a complete, production-ready MLOps platform on AWS
What to look for
- Hands-on SageMaker or Bedrock production experience
- Strong understanding of AWS IAM, VPC, and security for ML workloads
- Cost management experience for GPU and large-scale data workloads
Frequently asked questions
What AWS services are most important for AI companies?
SageMaker for training and deployment, S3 for data storage, ECR for container images, ECS or EKS for serving, and Bedrock for managed LLM APIs. Lambda is useful for lightweight inference and event-driven AI workflows.
Should I hire an AWS generalist or an ML-focused AWS engineer?
For AI infrastructure: an ML-focused AWS engineer is better. They understand GPU instance types, SageMaker specifics, and the cost profile of training and inference workloads — not just general cloud architecture.
Can AWS engineers help manage the cost of LLM inference?
Yes. AWS engineers with AI experience use Spot instances, SageMaker Savings Plans, and inference caching strategies to reduce costs significantly. Cost optimisation is often the highest-impact work for AI teams post-launch.
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