Role: MLOps Engineer
Location: San Francisco California
Duration: Long Term Contract
Key Responsibilities
- Develop and maintain ML pipelines using tools like MLflow Kubeflow or Vertex AI.
- Automate model training testing deployment and monitoring in cloud environments (e.g. GCP AWS Azure).
- Implement CI/CD workflows for model lifecycle management including versioning monitoring and retraining.
- Monitor model performance using observability tools and ensure compliance with model governance frameworks (MRM documentation explainability)
- Collaborate with engineering teams to provision containerized environments and support model scoring via low-latency APIs
- Leverage AutoML tools (e.g. Vertex AI AutoML H2O Driverless AI) for low-code/no-code model development documentation automation and rapid deployment
Skills Required:
- Develop and maintain ML pipelines using tools like MLflow Kubeflow or Vertex AI.
- Automate model training testing deployment and monitoring in cloud environments (e.g. GCP AWS Azure).
- Implement CI/CD workflows for model lifecycle management including versioning monitoring and retraining.
- Monitor model performance using observability tools and ensure compliance with model governance frameworks (MRM documentation explainability)
- Collaborate with engineering teams to provision containerized environments and support model scoring via low-latency APIs
- Leverage AutoML tools (e.g. Vertex AI AutoML H2O Driverless AI) for low-code/no-code model development documentation automation and rapid deployment
Role: MLOps Engineer Location: San Francisco California Duration: Long Term Contract Key Responsibilities Develop and maintain ML pipelines using tools like MLflow Kubeflow or Vertex AI. Automate model training testing deployment and monitoring in cloud environments (e.g. GCP AWS Azure). Impleme...
Role: MLOps Engineer
Location: San Francisco California
Duration: Long Term Contract
Key Responsibilities
- Develop and maintain ML pipelines using tools like MLflow Kubeflow or Vertex AI.
- Automate model training testing deployment and monitoring in cloud environments (e.g. GCP AWS Azure).
- Implement CI/CD workflows for model lifecycle management including versioning monitoring and retraining.
- Monitor model performance using observability tools and ensure compliance with model governance frameworks (MRM documentation explainability)
- Collaborate with engineering teams to provision containerized environments and support model scoring via low-latency APIs
- Leverage AutoML tools (e.g. Vertex AI AutoML H2O Driverless AI) for low-code/no-code model development documentation automation and rapid deployment
Skills Required:
- Develop and maintain ML pipelines using tools like MLflow Kubeflow or Vertex AI.
- Automate model training testing deployment and monitoring in cloud environments (e.g. GCP AWS Azure).
- Implement CI/CD workflows for model lifecycle management including versioning monitoring and retraining.
- Monitor model performance using observability tools and ensure compliance with model governance frameworks (MRM documentation explainability)
- Collaborate with engineering teams to provision containerized environments and support model scoring via low-latency APIs
- Leverage AutoML tools (e.g. Vertex AI AutoML H2O Driverless AI) for low-code/no-code model development documentation automation and rapid deployment
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