Remote Position
ML Operations Engineer
🌍 Remote · Poland
💼 Full-time
Job Description:
- The Machine Learning Operations (MLOps) Engineer will support our AI/ML initiatives by streamlining the deployment, monitoring, and scaling of machine learning models in production environments.
- Implement and maintain CI/CD pipelines for deploying machine learning models to production environments.
- Ensure seamless integration of machine learning models into existing software systems.
- Design and manage scalable infrastructure for training, testing, and serving machine learning models.
- Automate data preprocessing, model training, and deployment workflows.
- Monitor the performance of deployed models and systems, identifying and resolving issues proactively.
- Optimize model inference latency, scalability, and resource utilization.
- Work closely with data scientists, software engineers, and product teams to understand requirements and deliver operational solutions.
- Collaborate with DevOps and cloud engineering teams to ensure infrastructure reliability and security.
- Maintain version control for datasets, models, and code.
- Implement best practices for data and model governance, ensuring compliance with organizational and regulatory requirements.
- Stay updated with the latest trends in MLOps tools, frameworks, and practices.
- Recommend and implement improvements to the MLOps processes and infrastructure. Requirements:
- Education Required: Bachelor’s degree in Computer Science, Data Science, Engineering, or a related field.
- Experience Required: 2-3 years of hands-on experience in MLOps, DevOps, or related roles.
- Experience with MLOps tools and platforms like MLflow, Kubeflow, or SageMaker.
- Experience with feature stores and model versioning systems.
- Experience in building CI/CD pipelines using tools like Jenkins, GitLab CI, or similar.
- Knowledge of: Proficiency in Python and familiarity with containerization and orchestration tools (e.g., Docker, Kubernetes).
- Strong understanding of containerization and orchestration tools (e.g., Docker, Kubernetes).
- Familiarity with distributed computing frameworks (e.g., Apache Spark).
- Knowledge of cloud platforms such as AWS, Azure, or Google Cloud.
- Solid understanding of model monitoring, logging, and debugging tools.
- Familiarity with database technologies and data pipelines (SQL, NoSQL, ETL/ELT processes). Benefits: Apply tot his job