Position: Machine Learning Engineer
Company: GlobalLogic
Experience required: 5y
English level: upper

Our client is a multi-brand retailer based in Montreal, Canada specializing in the sale of designer fashion and high end streetwear. It was founded as an e-commerce platform in 2003.

Requirements:
Experience with SageMaker Studio and all features it provides (Sagemaker Pipelines)
Ability to create and adjust SageMaker pipelines for the end-to-end ML development cycle
Experience with deploying model inference with Sagemaker endpoints and other serverless computing (such as Lambda)
experience with transferring projects from on-premise to SageMaker
Ability to understand machine learning development and deployment processes
Good knowledge of how to deploy model inference with CodePipeline

Responsibilities:
Collaborate with client to understand their current machine learning development environment setup
Provide guidance and best practices to client on how to effectively and securely setup SageMaker Studio domain
design, implement and manage AWS infrastructure for the ML environment, including VPCs, subnets, security groups, and other AWS resources
Work with clients to configure AWS IAM roles, policies, and permissions to ensure secure access to AWS resources and data
identify opportunities for optimization and cost reduction in the AWS infrastructure and SageMaker Studio environments
Create reusable project templates to create the infrastructure for MLOps solution for CI/CD of ML models
Conduct workshops for client on how to use SageMaker Studio features effectively