ML-Ops Engineer
Tasks
- Automate model lifecycle workflows
- Build CI/CD pipelines for ML workflows
- Build ML pipelines
- Collaborate with data engineering on data processing alignment
- Collaborate with data scientists on productionizing models
- Deploy models to batch systems
- Deploy models to real time endpoints
- Ensure AWS security best practices
- Implement automated testing for data and model quality
- Implement data preprocessing and feature engineering workflows
- Implement model monitoring and drift detection
- Maintain AWS data and ML environments
- Provision AWS infrastructure as code
- Set up logging monitoring and observability
- Trigger automated model retraining
Perks/Benefits
- Accreditations
- Bike to work
- Educational assistance
- Employee assistance program
- Employee discount scheme
- Enhanced maternity/paternity leave
- Financial advice
- Fitness classes
- Flexible remote working
- Generous holiday allowance
- Gym discounts
- Incentivized certifications
- Life assurance
- Life insurance
- Marriage civil partnership leave
- Mentorship coaching
- Mindfulness workshops
- Pension
- Private healthcare
- Quarterly performance related profit share
- Special leave policies
Skills/Tech-stack
AWS CloudFormation | AWS CloudWatch | AWS CodePipeline | AWS Glue | AWS Lambda | AWS Step Functions | AWS VPC | Amazon CloudSearch | Amazon ECR | Amazon ECS | Amazon EKS | Amazon EMR | Amazon S3 | Amazon SageMaker | Amazon SageMaker Model Registry | Apache Spark | Cloud Native | Cloud-native Security | Docker | ELK | GitHub Actions | GitLab CI | Grafana | IAM | Jenkins | Kubernetes | Model Registry | Prometheus | Python | SageMaker Model Registry | Step Functions | Terraform
Education
N/A
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