MLOps Engineer - W2 Only
⭐ - Featured Role | Apply direct with Data Freelance Hub
This role is for an MLOps Engineer with a contract length of "Unknown," offering a pay rate of "Unknown." Key skills include Python, Docker, and cloud platforms (AWS, Azure, GCP). Experience in monitoring ML models and CI/CD practices is preferred.
🌎 - Country
United States
💱 - Currency
$ USD
-
💰 - Day rate
-
🗓️ - Date discovered
September 26, 2025
🕒 - Project duration
Unknown
-
🏝️ - Location type
Unknown
-
📄 - Contract type
W2 Contractor
-
🔒 - Security clearance
Unknown
-
📍 - Location detailed
Charlotte Metro
-
🧠 - Skills detailed
#Deployment #GCP (Google Cloud Platform) #TensorFlow #DevOps #PyTorch #AWS (Amazon Web Services) #ML (Machine Learning) #Kubernetes #MLflow #Cloud #Monitoring #Azure #Python #Docker #Bash #Scala #Airflow #Programming #Data Science #Documentation #Logging
Role description
Job Summary (MLOps Engineer)
• Develop, deploy, and maintain machine learning models in production environments.
• Design and implement robust MLOps pipelines for model training, testing, deployment, and monitoring.
• Collaborate with data scientists, software engineers, and DevOps teams to ensure seamless integration of ML models into production systems.
• Automate end-to-end ML workflows including data preprocessing, model training, validation, and deployment.
• Monitor and optimize ML models in production for performance, scalability, and reliability.
• Maintain comprehensive documentation for MLOps processes and best practices.
• Utilize ML frameworks (TensorFlow, PyTorch, scikit-learn) and MLOps tools (MLflow, Kubeflow, Airflow).
• Work with cloud platforms (AWS, Azure, GCP) for deploying and managing ML pipelines.
• Apply CI/CD practices, containerization (Docker), and orchestration (Kubernetes) in ML operations.
• Demonstrate strong programming skills in Python, Bash, or similar languages.
• Leverage excellent problem-solving, collaboration, and communication skills in a fast-paced environment.
• Preferably, possess experience in monitoring ML models (drift detection, logging, metrics), data versioning, feature stores, and reproducible ML workflows.
Job Summary (MLOps Engineer)
• Develop, deploy, and maintain machine learning models in production environments.
• Design and implement robust MLOps pipelines for model training, testing, deployment, and monitoring.
• Collaborate with data scientists, software engineers, and DevOps teams to ensure seamless integration of ML models into production systems.
• Automate end-to-end ML workflows including data preprocessing, model training, validation, and deployment.
• Monitor and optimize ML models in production for performance, scalability, and reliability.
• Maintain comprehensive documentation for MLOps processes and best practices.
• Utilize ML frameworks (TensorFlow, PyTorch, scikit-learn) and MLOps tools (MLflow, Kubeflow, Airflow).
• Work with cloud platforms (AWS, Azure, GCP) for deploying and managing ML pipelines.
• Apply CI/CD practices, containerization (Docker), and orchestration (Kubernetes) in ML operations.
• Demonstrate strong programming skills in Python, Bash, or similar languages.
• Leverage excellent problem-solving, collaboration, and communication skills in a fast-paced environment.
• Preferably, possess experience in monitoring ML models (drift detection, logging, metrics), data versioning, feature stores, and reproducible ML workflows.
