Machine Learning Engineer
Tasks
- Apply security reliability software engineering best practices
- Build scalable machine learning pipelines
- Containerize and orchestrate ML workloads
- Create technical documentation and support architecture discussions
- Deploy monitor optimize machine learning models
- Develop production Python services
- Improve CI CD workflows for ML applications
- Refactor and optimize codebases
- Set up and automate AI platform infrastructure on AWS SageMaker or GCP AI Platform
- Transition experiments to production solutions
Perks/Benefits
- Career development
- Internal R D projects
- International mobility
- Knowledge sharing
- Personalized training
Skills/Tech-stack
AI Platform | AWS SageMaker | Agile | Apache Airflow | Azure Repos | CI/CD | Cloud AI Platform | Data Versioning | Docker | Feature Store | Generative AI | Git | GitLab CI | Google Cloud | Google Cloud AI | Google Cloud AI Platform | Infrastructure as Code | Jenkins | Kubeflow | Kubernetes | LLMOps | MLflow | Model Monitoring | Observability | Python | “as-code”
Education
N/A
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