PIXIE
MLops Engineer
⭐ - Featured Role | Apply direct with Data Freelance Hub
This role is for an MLops Engineer with a contract length of "unknown" and a pay rate of "unknown." Key skills include AWS SageMaker, Python, and machine learning frameworks. Experience in MLOps and AWS certification is preferred. Work location is "unknown."
🌎 - Country
United States
💱 - Currency
$ USD
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💰 - Day rate
Unknown
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🗓️ - Date
October 28, 2025
🕒 - 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
United States
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🧠 - Skills detailed
#Deployment #Security #Cloud #Data Engineering #SageMaker #Scala #TensorFlow #Lambda (AWS Lambda) #S3 (Amazon Simple Storage Service) #Python #ML (Machine Learning) #AWS (Amazon Web Services) #Model Optimization #AWS SageMaker #Compliance #Data Science #PyTorch
Role description
We are looking for a skilled AWS SageMaker professional to develop, deploy, and manage machine learning models on the AWS cloud platform. The ideal candidate will have hands-on experience with SageMaker and a solid understanding of ML lifecycle management, data preprocessing, and model optimization.
Key Responsibilities:
• Build, train, and deploy machine learning models using AWS SageMaker.
• Automate ML workflows and manage model versioning and deployment pipelines.
• Collaborate with data scientists and data engineers to integrate ML models into production.
• Optimize performance and scalability of ML solutions on AWS.
• Ensure security, compliance, and cost-efficiency in cloud ML deployments.
Required Skills:
• Proven experience with AWS SageMaker and related AWS services (Lambda, S3, CloudWatch, etc.).
• Strong knowledge of machine learning frameworks (e.g., TensorFlow, PyTorch, Scikit-learn).
• Proficiency in Python and data preprocessing techniques.
• Familiarity with CI/CD practices for ML.
Preferred Qualifications:
• AWS Certified Machine Learning – Specialty or equivalent certification.
• Experience with MLOps and SageMaker Pipelines.
We are looking for a skilled AWS SageMaker professional to develop, deploy, and manage machine learning models on the AWS cloud platform. The ideal candidate will have hands-on experience with SageMaker and a solid understanding of ML lifecycle management, data preprocessing, and model optimization.
Key Responsibilities:
• Build, train, and deploy machine learning models using AWS SageMaker.
• Automate ML workflows and manage model versioning and deployment pipelines.
• Collaborate with data scientists and data engineers to integrate ML models into production.
• Optimize performance and scalability of ML solutions on AWS.
• Ensure security, compliance, and cost-efficiency in cloud ML deployments.
Required Skills:
• Proven experience with AWS SageMaker and related AWS services (Lambda, S3, CloudWatch, etc.).
• Strong knowledge of machine learning frameworks (e.g., TensorFlow, PyTorch, Scikit-learn).
• Proficiency in Python and data preprocessing techniques.
• Familiarity with CI/CD practices for ML.
Preferred Qualifications:
• AWS Certified Machine Learning – Specialty or equivalent certification.
• Experience with MLOps and SageMaker Pipelines.
