Charter Global
ML Ops Engineer
⭐ - Featured Role | Apply direct with Data Freelance Hub
This role is for an ML Ops Engineer on a contract basis, requiring 5+ years in MLOps, AWS expertise, CI/CD automation, and containerization. Local candidates from New York, NY, or Charlotte, NC, preferred. Experience in financial services is a plus.
🌎 - Country
United States
💱 - Currency
$ USD
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💰 - Day rate
Unknown
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🗓️ - Date
October 24, 2025
🕒 - Duration
Unknown
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🏝️ - Location
Remote
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📄 - Contract
Unknown
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🔒 - Security
Unknown
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📍 - Location detailed
New York, United States
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🧠 - Skills detailed
#Data Engineering #GitLab #Automation #ML (Machine Learning) #AWS (Amazon Web Services) #PySpark #Infrastructure as Code (IaC) #Kubernetes #Lambda (AWS Lambda) #Terraform #Data Science #Jenkins #Spark (Apache Spark) #Computer Science #S3 (Amazon Simple Storage Service) #SageMaker #ECR (Elastic Container Registery) #ML Ops (Machine Learning Operations) #GitHub #Cloud #Docker #Deployment
Role description
Job Title ML Ops Engineer
Location: Remote/WFH/(New York, NY, USA, and Charlotte, NC, USA)
Duration: Contract
The position is remote, but the candidate must be willing to travel the first five days to New York, NY, USA, and Charlotte, NC, USA, so local candidates from these areas are preferred
Contract Description:
• MLOps / ML Engineering: 5+ years of experience in MLOps or ML Engineering.
• AWS Expertise: Strong knowledge of AWS services, particularly SageMaker, Lambda, ECR, ECS/EKS, S3, and Step Functions.
• CI/CD Automation: Hands-on experience with CI/CD tools for ML, such as GitHub Actions, GitLab, Jenkins, or CodePipeline.
• Containerization: Proficiency in containerization and deployment using Docker and Kubernetes.
• ML Pipeline Orchestration: Experience with ML pipeline orchestration tools like SageMaker Pipelines or Kubeflow.
Qualifications:
• Educational Background: A degree in Computer Science, Data Science, or a related field.
• Financial Services Experience: Previous experience in the financial services industry is a plus.
• Model Lifecycle Management: Experience in implementing enterprise-grade model lifecycle management.
• Data Engineering: Familiarity with data engineering workflows using tools like Glue, EMR, Spark, or PySpark.
• Infrastructure as Code: Knowledge of Infrastructure as Code tools such as Terraform or AWS CloudFormation.
• The position is remote, but the candidate must be willing to travel the first five days to New York, NY, USA, and Charlotte, NC, USA, so local candidates from these areas are preferred.
Job Title ML Ops Engineer
Location: Remote/WFH/(New York, NY, USA, and Charlotte, NC, USA)
Duration: Contract
The position is remote, but the candidate must be willing to travel the first five days to New York, NY, USA, and Charlotte, NC, USA, so local candidates from these areas are preferred
Contract Description:
• MLOps / ML Engineering: 5+ years of experience in MLOps or ML Engineering.
• AWS Expertise: Strong knowledge of AWS services, particularly SageMaker, Lambda, ECR, ECS/EKS, S3, and Step Functions.
• CI/CD Automation: Hands-on experience with CI/CD tools for ML, such as GitHub Actions, GitLab, Jenkins, or CodePipeline.
• Containerization: Proficiency in containerization and deployment using Docker and Kubernetes.
• ML Pipeline Orchestration: Experience with ML pipeline orchestration tools like SageMaker Pipelines or Kubeflow.
Qualifications:
• Educational Background: A degree in Computer Science, Data Science, or a related field.
• Financial Services Experience: Previous experience in the financial services industry is a plus.
• Model Lifecycle Management: Experience in implementing enterprise-grade model lifecycle management.
• Data Engineering: Familiarity with data engineering workflows using tools like Glue, EMR, Spark, or PySpark.
• Infrastructure as Code: Knowledge of Infrastructure as Code tools such as Terraform or AWS CloudFormation.
• The position is remote, but the candidate must be willing to travel the first five days to New York, NY, USA, and Charlotte, NC, USA, so local candidates from these areas are preferred.
