Back to SearchSenior Data DevOps Engineer (MLOps)
Remote in Armenia, & 4 others
Data DevOps& 9 others
Looking for something else?
Find a vacancy that works for you. Send us your CV to receive a personalized offer.
Find me a job
Choose an option
We are seeking a Senior Data DevOps Engineer (MLOps) with strong machine learning and Google Cloud Platform experience to design, develop, and implement continuous deployment pipelines for ML models.
Responsibilities
- Design, develop, and implement MLOps pipelines for the continuous deployment and integration of ML models
- Collaborate with data scientists to understand model requirements and optimize deployment processes
- Automate the training, testing, and deployment processes for machine learning models
- Monitor and maintain models, ensuring optimal performance, accuracy, and reliability
- Implement best practices for version control, model reproducibility, and governance
- Optimize machine learning pipelines for scalability, efficiency, and cost-effectiveness
- Troubleshoot and resolve issues related to model deployment and performance
- Ensure compliance with security and data privacy standards in all MLOps activities
- Stay up-to-date with the latest MLOps tools, technologies, and trends
- Provide support and guidance to other team members on MLOps practices and performance feedback
Requirements
- Bachelor's degree in Computer Science, Data Science, or a related field
- 3+ years of experience in MLOps, DevOps, or a related field
- Understanding of machine learning principles and model lifecycle management
- Proficiency in Python, with hands-on experience in TensorFlow, PyTorch, or Scikit-learn
- Knowledge of CI/CD pipelines, automation tools, and version control systems like Git
- Strong problem-solving skills and ability to troubleshoot complex issues
- Experience with monitoring tools and practices for model performance in production
- Ability to work collaboratively in cross-functional teams
- Experience with Google Cloud Platform and its respective machine learning services
- Familiarity with containerization and orchestration tools such as Composer and Kubernetes
- Knowledge of cloud data platform architecture, infrastructure, maintenance, and optimization