MLOps Engineering Specialist
Tasks
- Apply security least privilege with IAM
- Build scalable real time inference endpoints
- Build serverless inference endpoints
- Create automated testing for ML releases
- Deploy machine learning models to production
- Design end-to-end MLOps workflows
- Detect bias drift and feature attribution drift
- Enforce release governance and quality gates
- Implement VPC network isolation
- Implement automated retraining strategies
- Implement drift detection and evaluation routines
- Implement rollback strategies
- Manage features with SageMaker Feature Store
- Monitor data quality and model quality
- Orchestrate training with SageMaker Pipelines
- Productionize notebooks into managed pipelines
- Promote models across environments
- Report model performance metrics
- Set alerting and automated incident responses
- Track lineage with SageMaker Experiments
- Version models with SageMaker Model Registry
Perks/Benefits
- Discounted mobile and broadband
- Equalized maternity paternity adoption leave
- Gym memberships
- Holiday purchase scheme
- Online private GP 24 7
- Paid carer’s leave
- Pension scheme
Skills/Tech-stack
AWS | AWS CDK | AWS CloudFormation | Alerting | Amazon SageMaker | Bias detection | CI/CD | Distributed tracing | Docker | ECR | Feature Engineering | Feature Store | Glue | IAM | Lambda | Model Explainability | Model Governance | Model Registry | Monitoring | OpenTelemetry | S3 | SageMaker Experiments | SageMaker Feature Store | SageMaker Model Registry | Sagemaker Pipelines | Step Functions | Terraform | VPC | VPC Networking
Education
N/A
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