MLOPs Engineer
1 week ago
Responsibilities
- Develop and maintain CI/CD pipelines for machine learning models.
- Automate model deployment, monitoring, and scaling processes.
- Implement and manage version control for code, data, and models.
- Ensure data quality, security, and compliance throughout the ML lifecycle.
- Collaborate with data scientists, engineers, and other stakeholders.
- Optimize infrastructure for ML workloads.
- Troubleshoot and resolve issues in production ML systems.
- Implement logging, monitoring, and alerting for ML pipelines.
- Manage and optimize cloud resources for ML/AI workloads.
- Facilitate knowledge sharing and best practices across teams.
- Mentors peer developers on ML Ops and Dev Ops
Technical Competencies
- Programming languages: Python or similar
- DevOps tools: Docker, Kubernetes, Jenkins, GitLab CI, or Azure DevOps
- Cloud platforms: AWS, Azure, or Google Cloud Platform (AWS preferred)
- Infrastructure as Code: Terraform, Ansible, or CloudFormation
- ML frameworks: TensorFlow, PyTorch, or scikit-learn
- Big data technologies: Spark, Hadoop, or Kafka
- Monitoring and logging: ELK stack or similar
- Version control: Git
- Database management: SQL and NoSQL databases
- Data pipeline tools: Airflow, Kubeflow or similar
- Containerization and orchestration
- CI/CD methodologies and tools
- Understanding of ML algorithms and model serving
- Knowledge of data privacy and security best practices
- Familiarity with MLOps principles and tools (e.g., MLflow, DVC)
General Competencies:
- Advanced customer-focus skills.
- Advanced oral and written communication skills.
- Advanced interpersonal skills.
- Advanced decision-making skills.
- Advanced organization and prioritization skills.
- Advanced meeting facilitation skills.
- Advanced team-building skills.
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