MLOps Engineer
⭐ - Featured Role | Apply direct with Data Freelance Hub
This role is for an MLOps Engineer with 5+ years of experience in MLOps or ML Engineering, deep AWS expertise, proficiency in Terraform, and strong CI/CD skills. Contract length and pay rate are unspecified. Remote work is available.
🌎 - Country
United States
💱 - Currency
$ USD
💰 - Day rate
Unknown
Unknown
🗓️ - Date discovered
May 10, 2025
🕒 - Project duration
Unknown
🏝️ - Location type
Unknown
📄 - Contract type
Unknown
🔒 - Security clearance
Unknown
📍 - Location detailed
New Jersey, United States
🧠 - Skills detailed
#SageMaker #ML (Machine Learning) #S3 (Amazon Simple Storage Service) #Scripting #Compliance #Cloud #Monitoring #Jenkins #Docker #AWS (Amazon Web Services) #Lambda (AWS Lambda) #Bash #GitHub #IAM (Identity and Access Management) #Kubernetes #DevOps #Automation #Python #Terraform
Role description
• 5+ years of experience in MLOps, DevOps, or ML Engineering roles, preferably in cloud-native environments.
• Deep expertise in AWS services (e.g., SageMaker, EKS, S3, Lambda, Step Functions, IAM, CloudWatch).
• Advanced proficiency with Terraform and modular infrastructure development.
• Strong background in CI/CD for ML systems (GitHub Actions, CodePipeline, Jenkins, or similar).
• Proven experience with Docker and orchestration tools (e.g., Kubernetes, EKS).
• Solid scripting and automation skills in Python, Bash, or Go.
• Experience deploying and monitoring ML models in production environments.
• Familiarity with model governance, data versioning, lineage, and compliance frameworks.
• 5+ years of experience in MLOps, DevOps, or ML Engineering roles, preferably in cloud-native environments.
• Deep expertise in AWS services (e.g., SageMaker, EKS, S3, Lambda, Step Functions, IAM, CloudWatch).
• Advanced proficiency with Terraform and modular infrastructure development.
• Strong background in CI/CD for ML systems (GitHub Actions, CodePipeline, Jenkins, or similar).
• Proven experience with Docker and orchestration tools (e.g., Kubernetes, EKS).
• Solid scripting and automation skills in Python, Bash, or Go.
• Experience deploying and monitoring ML models in production environments.
• Familiarity with model governance, data versioning, lineage, and compliance frameworks.
