Senior Machine Learning Ops Engineer
Tasks
- Build containerized model serving with Docker and FastAPI
- Create retraining scoring and inference pipelines
- Deploy and maintain training and inference workloads
- Design scalable ML infrastructure
- Document architecture deployment and operational standards
- Establish CI CD and deployment automation
- Implement infrastructure-as-code
- Implement orchestration workflows for ML pipelines
- Lead cloud native hosting architecture in AWS
- Monitor model performance drift and data quality
- Optimize reliability scalability and operational efficiency
- Set up observability monitoring and alerting for ML systems
Perks/Benefits
Skills/Tech-stack
AWS | Airflow | Bash | Batch inference | CI/CD | DBT | Dagster | Data Quality | Docker | Drift Detection | ECS | EKS | FastAPI | Infrastructure as Code | Kubernetes | MLOps | Machine Learning | Model Drift | Model Drift Detection | Monitoring | Observability | Prefect | Pytest | Python | Real Time | Real-time Inference | SQL | Snowflake | Terraform | Testing | Unix | “as-code”
Education
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