Posted:2 weeks ago|
Platform:
Hybrid
Full Time
Role Summary
(Purpose: why does this role exist?)
We are seeking an experienced MLOps Engineer specializing in AWS to architect,
implement, and maintain robust machine learning operations infrastructure. This
role is crucial in bridging the gap between data science and production deployment,
ensuring seamless delivery of ML solutions at scale.
The ideal candidate will be responsible for building and maintaining scalable,
automated ML pipelines, implementing CI/CD practices, and optimizing cloud
infrastructure for ML workloads. They will work closely with data scientists and
software engineers to streamline the model deployment process, establish
monitoring systems, and ensure high availability of ML services.
Handling MLOps Infrastructure
Design and implement end-to-end ML pipelines on AWS
Set up and maintain CI/CD pipelines for ML models
Automate model deployment, monitoring, and scaling
Implement data versioning and model registry systems
Establish MLOps best practices and standards
Development & Implementation
Build robust ML deployment architectures using AWS services
Develop automated testing frameworks for ML systems
Create monitoring and alerting systems for ML models
Implement feature stores and model serving platforms
Optimize infrastructure costs and performance
Platform Management
Manage AWS infrastructure using IaC (Terraform/CloudFormation)
Configure and maintain container orchestration (EKS/ECS)
Set up model experiment tracking systems
Implement security best practices
Handle capacity planning and resource optimization
Quest Global
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