DevOps engineer building full - stack automation tools for General Motors’ automotive software delivery. Improving CI/CD, testing, quality monitoring, and engineering workflows.
About the role
- Part-time Remote DevOps/MLOps Engineer at Scopic designing and maintaining scalable infrastructure and automation pipelines for machine learning workflows. Collaborating with engineering and data teams in a flexible work environment.
Responsibilities
- Design and implement CI/CD pipelines for both general applications and machine learning workflows
- Build and maintain scalable infrastructure using containerization (Docker, Kubernetes) and infrastructure-as-code tools
- Manage and optimize cloud-based environments across AWS, GCP, or Azure
- Deploy, monitor, and maintain machine learning models and pipelines in production environments
- Establish and improve monitoring, logging, and alerting systems for application and ML infrastructure health
- Automate deployment, scaling, and operational tasks to reduce manual overhead and improve reliability
- Collaborate with software engineers and data scientists to understand deployment requirements and optimize system architecture
- Ensure security, performance, and reliability across infrastructure and application layers
- Troubleshoot and resolve infrastructure, deployment, and operational issues
- Maintain documentation and best practices for infrastructure and deployment processes
Requirements
- 3+ years of hands-on DevOps or infrastructure engineering experience
- Strong proficiency with containerization and orchestration (Docker, Kubernetes)
- Experience with infrastructure-as-code tools such as Terraform, CloudFormation, or Ansible
- Solid understanding of CI/CD concepts and hands-on experience with tools like Jenkins, GitLab CI/CD, GitHub Actions, or similar
- Strong programming skills in Python, Bash, or Go for automation and scripting
- Hands-on experience with at least one major cloud platform (AWS, GCP, or Azure)
- Familiarity with monitoring and logging tools (Prometheus, ELK stack, CloudWatch, or similar)
- Experience with ML operationalization and deployment platforms such as MLflow, Kubeflow, SageMaker, or Vertex AI is a plus
- Understanding of ML frameworks and workflows (TensorFlow, PyTorch, scikit-learn) is a plus
- Strong problem-solving skills and ability to work independently on infrastructure challenges
Benefits
- Paid training and other professional opportunities
- Flexibility and freedom to work from anywhere with a stable internet connection
- Consistent flow of engaging, challenging work to do
- Annual pay increases for good performance
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