PineQ Lab Technology
ML OPS Engineer
⭐ - Featured Role | Apply direct with Data Freelance Hub
This role is for an ML Ops Engineer with strong Python skills and experience in TensorFlow, PyTorch, or Scikit-learn. Contract length is "unknown," pay rate is "unknown," and work location is "unknown." Key skills include Docker, Kubernetes, and cloud platforms (AWS, Azure, GCP).
🌎 - Country
United States
💱 - Currency
$ USD
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💰 - Day rate
Unknown
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🗓️ - Date
June 11, 2026
🕒 - Duration
Unknown
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🏝️ - Location
Unknown
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📄 - Contract
Unknown
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🔒 - Security
Unknown
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📍 - Location detailed
Sunnyvale, CA
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🧠 - Skills detailed
#Deployment #SageMaker #Agile #ML (Machine Learning) #AI (Artificial Intelligence) #Python #Azure #AWS (Amazon Web Services) #TensorFlow #ML Ops (Machine Learning Operations) #MLflow #GitLab #Kubernetes #GitHub #GCP (Google Cloud Platform) #Jenkins #GIT #PyTorch #Azure DevOps #Docker #Model Deployment #DevOps #Monitoring #Cloud
Role description
Strong experience with Python and machine learning frameworks such as TensorFlow, PyTorch, or Scikit-learn.
Experience with Docker and Kubernetes.
Hands-on experience with cloud platforms (AWS, Azure, or GCP).
Knowledge of CI/CD tools such as Jenkins, GitHub Actions, GitLab CI, or Azure DevOps.
Experience with MLflow, Kubeflow, SageMaker, Vertex AI, or Azure ML.
Strong understanding of model deployment, monitoring, and versioning.
Experience with Git and Agile development methodologies.
Strong experience with Python and machine learning frameworks such as TensorFlow, PyTorch, or Scikit-learn.
Experience with Docker and Kubernetes.
Hands-on experience with cloud platforms (AWS, Azure, or GCP).
Knowledge of CI/CD tools such as Jenkins, GitHub Actions, GitLab CI, or Azure DevOps.
Experience with MLflow, Kubeflow, SageMaker, Vertex AI, or Azure ML.
Strong understanding of model deployment, monitoring, and versioning.
Experience with Git and Agile development methodologies.
