AWS Architect - ML & SageMaker
Job Title: AWS Architect – ML & SageMaker
Location: FULLY Remote
Position type: Long term Contract
Rate: DOE
Description:-
We are seeking an experienced AWS Architect with strong hands-on expertise in Amazon SageMaker to design and implement scalable, secure, and production-grade ML platforms on AWS.
The role will own architecture decisions across data ingestion, model training, deployment, MLOps, and governance.
Key Responsibilities:
Architect end-to-end ML platforms using SageMaker and core AWS services
Design scalable training and inference pipelines (real-time & batch)
Implement CI/CD for ML workflows and model versioning
Establish MLOps best practices (monitoring, drift detection, rollback strategies)
Define secure, multi-account AWS architecture and networking patterns
Optimize cost, performance, and reliability of ML workloads
Guide engineering teams on cloud-native and microservices-based ML design
Required Skills & Experience:
8+ years in cloud architecture; 3+ years hands-on with SageMaker
Strong experience with:
Amazon S3
AWS Lambda
Amazon EKS or container orchestration
AWS CodePipeline
Proficiency in Python and ML frameworks (TensorFlow / PyTorch / scikit-learn)
Experience with Docker and Infrastructure as Code (Terraform/CloudFormation)
Strong understanding of distributed systems and event-driven architectures
Good to Have:
Experience with GenAI/ LLM deployment
Feature store and model registry implementation
Skills Matrix:-
Total work experience in IT Industry: Years
Total work experience in USA: Years
AWS Architect: Years
ML: Years
SageMaker: Years
Amazon S3: Years
AWS Lambda: Years
Amazon EKS or Container Orchestration: Years
AWS CodePipeline: Years
Python and ML frameworks (TensorFlow / PyTorch / scikit-learn): Years
Experience with Docker and Infrastructure as Code (Terraform/CloudFormation): Years
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Apply To this Job
Location: FULLY Remote
Position type: Long term Contract
Rate: DOE
Description:-
We are seeking an experienced AWS Architect with strong hands-on expertise in Amazon SageMaker to design and implement scalable, secure, and production-grade ML platforms on AWS.
The role will own architecture decisions across data ingestion, model training, deployment, MLOps, and governance.
Key Responsibilities:
Architect end-to-end ML platforms using SageMaker and core AWS services
Design scalable training and inference pipelines (real-time & batch)
Implement CI/CD for ML workflows and model versioning
Establish MLOps best practices (monitoring, drift detection, rollback strategies)
Define secure, multi-account AWS architecture and networking patterns
Optimize cost, performance, and reliability of ML workloads
Guide engineering teams on cloud-native and microservices-based ML design
Required Skills & Experience:
8+ years in cloud architecture; 3+ years hands-on with SageMaker
Strong experience with:
Amazon S3
AWS Lambda
Amazon EKS or container orchestration
AWS CodePipeline
Proficiency in Python and ML frameworks (TensorFlow / PyTorch / scikit-learn)
Experience with Docker and Infrastructure as Code (Terraform/CloudFormation)
Strong understanding of distributed systems and event-driven architectures
Good to Have:
Experience with GenAI/ LLM deployment
Feature store and model registry implementation
Skills Matrix:-
Total work experience in IT Industry: Years
Total work experience in USA: Years
AWS Architect: Years
ML: Years
SageMaker: Years
Amazon S3: Years
AWS Lambda: Years
Amazon EKS or Container Orchestration: Years
AWS CodePipeline: Years
Python and ML frameworks (TensorFlow / PyTorch / scikit-learn): Years
Experience with Docker and Infrastructure as Code (Terraform/CloudFormation): Years
Apply tot his job
Apply To this Job