Cyber Space Technologies LLC
MLops Lead
⭐ - Featured Role | Apply direct with Data Freelance Hub
This role is for an MLops Lead with a contract length of "unknown," offering a pay rate of "unknown." Key skills include AWS MLOps solutions, Python coding, automation tools, and experience with ML frameworks like PyTorch or TensorFlow.
🌎 - Country
United States
💱 - Currency
$ USD
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💰 - Day rate
Unknown
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🗓️ - Date
October 7, 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
Plano, TX
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🧠 - Skills detailed
#AWS (Amazon Web Services) #Monitoring #Terraform #Lambda (AWS Lambda) #DevOps #Documentation #Argo #Data Science #ML (Machine Learning) #Cloud #Airflow #PyTorch #SNS (Simple Notification Service) #GitHub #Docker #Automation #S3 (Amazon Simple Storage Service) #TensorFlow #Data Engineering #IAM (Identity and Access Management) #Infrastructure as Code (IaC) #Python #MLflow #SQS (Simple Queue Service) #SageMaker
Role description
Acceptance Criteria:
• Cloud Expertise: Extensive hands-on experience in designing and implementing MLOps solutions on AWS. Proficient with core services like SageMaker, S3, ECS, EKS, Lambda, SQS, SNS, and IAM.
• Coding & Automation: Strong coding proficiency in Python. Extensive experience with automation tools, including Terraform for IaC and GitHub Actions.
• MLOps & DevOps: A solid understanding of MLOps and DevOps principles.
• Hands-on experience with MLOps frameworks like Sagemaker Pipelines, Model Registry, Weights and Bias, MLflow or Kubeflow and orchestration tools like Airflow or Argo Workflows.
Containerization: Expertise in developing and deploying containerized applications using Docker and orchestrating them with ECS and EKS.
• Model Lifecycle: Experience with model testing, validation, and performance monitoring. Good understanding of ML frameworks like PyTorch or TensorFlow is required to effectively collaborate with data scientists.
• Communication: Excellent communication and documentation skills, with a proven ability to collaborate with cross-functional teams (data scientists, data engineers, and architects).
Acceptance Criteria:
• Cloud Expertise: Extensive hands-on experience in designing and implementing MLOps solutions on AWS. Proficient with core services like SageMaker, S3, ECS, EKS, Lambda, SQS, SNS, and IAM.
• Coding & Automation: Strong coding proficiency in Python. Extensive experience with automation tools, including Terraform for IaC and GitHub Actions.
• MLOps & DevOps: A solid understanding of MLOps and DevOps principles.
• Hands-on experience with MLOps frameworks like Sagemaker Pipelines, Model Registry, Weights and Bias, MLflow or Kubeflow and orchestration tools like Airflow or Argo Workflows.
Containerization: Expertise in developing and deploying containerized applications using Docker and orchestrating them with ECS and EKS.
• Model Lifecycle: Experience with model testing, validation, and performance monitoring. Good understanding of ML frameworks like PyTorch or TensorFlow is required to effectively collaborate with data scientists.
• Communication: Excellent communication and documentation skills, with a proven ability to collaborate with cross-functional teams (data scientists, data engineers, and architects).
