AWS Solutions Architect (AI Implementations)
Tasks
- Conduct Well-Architected reviews
- Conduct architecture and code reviews
- Define Infrastructure as Code standards
- Design data architecture for AI workloads
- Design end to end AWS architectures for AI and GenAI
- Ensure security and compliance alignment
- Lead architecture discovery workshops
- Mentor cloud engineers
- Review infrastructure implementations
- Select configure AWS AI ML services
- Stay current on AWS AI ML roadmap
- Support pre-sales solutioning
Perks/Benefits
Skills/Tech-stack
AI/ML Services | API Gateway | AWS AI | AWS AI/ML | AWS AI/ML services | AWS CDK | AWS CloudFormation | AWS Organizations | AWS X-Ray | Access patterns | Account architecture | Amazon Aurora | Amazon Bedrock | Amazon CloudWatch | Amazon Comprehend | Amazon DynamoDB | Amazon EFS | Amazon RDS | Amazon S3 | Amazon SageMaker | Amazon Textract | Amazon Web Services | CloudFront | Control Tower | Data Ingestion | Data Lakes | Data Storage | Data Transformation | Data access | Data access patterns | Docker | Drift Detection | EC2 | ECS | EKS | Encryption | Feature Store | FinOps | GDPR | Helm | IAM | Infrastructure as Code | Kubernetes | LLM integration | Lambda | Landing Zone | Langchain | Langgraph | ML services | MLOps | MLflow | Model Deployment | Model Drift | Model Drift Detection | Model Monitoring | Model versioning | Monitoring | Multi-account | Multi-account architecture | Observability | OpenTelemetry | RAG | Reserved Instances | Retrieval-Augmented Generation | Rightsizing | Sagemaker Pipelines | Savings Plans | Streaming Pipelines | Terraform | VPC | Vector Databases | Web Services | X-Ray | “as-code”
Education
N/A
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