MLOps Engineer
Tasks
- Automate training and retraining
- Build ML data and feature pipelines
- Build and evolve MLOps platform
- Build model observability and monitoring
- Capture production signals and expert feedback
- Deploy models with safe rollout and rollback
- Drive infrastructure reliability scalability improvements
- Feed feedback into evaluation and retraining workflows
- Implement human-in-the-loop systems
- Maintain model registry and packaging
- Operate batch streaming and real time model serving
- Orchestrate data pipelines and workflows
- Own experiment to production pipeline
- Set up lakehouse data foundation with lineage and versioning
- Support feature store capabilities
Perks/Benefits
Skills/Tech-stack
Active Learning | Apache Airflow | Apache Iceberg | Batch inference | CI/CD | Containers | Dagster | Data Lineage | Data Quality | Data labeling | Experiment tracking | Feature Store | Human-in-the-loop | Image data | Infrastructure as Code | Kubernetes | Lakehouse | MLflow | Machine Learning | Model Observability | Model Registry | Multimodal Machine Learning | Python | Real Time | Real-time Inference | S3 | SQL | Series data | Streaming inference | The Loop | Time Series | Time Series Data | Workflow Orchestration | “as-code”
Education
Bachelor of Engineering | Bachelor of Science | Master of Science | PhD
Roles
Regions
Countries
States
Cities
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