ML Engineer
Tasks
- Administer Databricks Airflow and Snowflake
- Collaborate with data science data engineering and cloud teams
- Coordinate MLOps implementation across regions
- Create CI/CD pipelines for cloud deployment
- Design ML pipelines
- Ensure code quality and reproducibility
- Execute MLOps strategy
- Implement model training deployment monitoring and retraining
- Manage Docker containers and Kubernetes orchestration
- Standardize repository and documentation conventions
- Use Git-based version control
Perks/Benefits
- Birthday gift voucher
- Childcare support
- Coaching
- Digital reading app access
- Emotional wellbeing support
- Enhanced vacation
- Flexible compensation plan
- Flexible work schedule
- Psychological support
- Sabbatical periods
- Ticket Restaurant subsidy
Skills/Tech-stack
AWS | Apache Airflow | Azure | Bash | CD pipelines | CI/CD | CI/CD pipelines | Data Pipelines | Databricks | Docker | Git | GitLab | Kubernetes | LLMOps | Linux | MLflow | Machine Learning | Python | Snowflake
Education
N/A
Roles
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