Overview
Hybrid
Depends on Experience
Full Time
50% Travel
Skills
TensorFlow
PyTorch
Scikit-learn
Kubeflow
MLflow
Airflow
TFX
Terraform
Docker
Kubernetes
Job Details
Position: MLOps Engineer Location: Sunnyvale, CA Onsite Type: Contract
Must have: Expertise in Python and experience with ML frameworks (TensorFlow, PyTorch, Scikit-learn, etc.). Strong Experience in deployment/devops technologies: CI/CD pipelines, Kubernetes/Docker, and infrastructure-as-code tools (Terraform, Ansible, etc.). and cloud-native architectures (Google Cloud Platform and Aruze), monitoring and observability for ML workloads Advanced understanding of ML pipeline orchestration tools like Kubeflow, MLflow, Airflow, or TFX.
Nice to have:
Experience with distributed computing frameworks (e.g., Spark, Ray, Dask) is a plus.
Familiarity with model explainability, fairness, and bias detection tools is highly desirable.
Strong knowledge of security best practices for ML systems, including data encryption, API security, and governance.
Must have: Expertise in Python and experience with ML frameworks (TensorFlow, PyTorch, Scikit-learn, etc.). Strong Experience in deployment/devops technologies: CI/CD pipelines, Kubernetes/Docker, and infrastructure-as-code tools (Terraform, Ansible, etc.). and cloud-native architectures (Google Cloud Platform and Aruze), monitoring and observability for ML workloads Advanced understanding of ML pipeline orchestration tools like Kubeflow, MLflow, Airflow, or TFX.
Nice to have:
Experience with distributed computing frameworks (e.g., Spark, Ray, Dask) is a plus.
Familiarity with model explainability, fairness, and bias detection tools is highly desirable.
Strong knowledge of security best practices for ML systems, including data encryption, API security, and governance.
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